Revolutionizing Sepsis Diagnosis Using Machine Learning and Deep Learning Models: A Systematic Literature Review 

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The complexity and rapid growth of sepsis make it challenging to detect in its early phases using traditional techniques. Thus, machine learning (ml) and deep learning (dl) methods have emerged as promising tools, offering the potential to process vast amounts of electronic health data, detect patterns, and predict the onset of sepsis earlier than conventional techniques. This systematic review critically examines the use of ML and DL models for sepsis detection and prediction with application across diverse clinical datasets i.e. Electronic Health Records (EHRs), vital signs monitoring systems, and large-scale databases. Through a comprehensive search of the relevant literature, this review synthesizes findings from over 125 studies, exploring the effectiveness of various computational methods more than 1500. The process of systematic literature review SLR included accessing articles from IEEE, ACM, and Scopus, with deletion of duplicate papers, articles from languages other than English, and outdated studies that resulted in only 80 valid studies. These methods range from simpler algorithms i.e. decision trees and support vector machines, to other models i.e., neural networks and ensemble techniques. Each model's capacity to handle the complexity of sepsis data is thoroughly analyzed. Besides this, the review also highlights key challenges inside the field, data quality problems, the generalization of models throughout patients with different populations, and ethical considerations related. These challenges pose barriers to the adoption of ML and DL for sepsis recognition in real-world clinical settings. In conclusion, the study highlights the need for advanced feature engineering, the use of ensemble techniques, advancement of integrated and real-time sepsis prediction systems. Such models improve accuracy, robustness, and scalability of predictive models for effective interventions. By addressing the current limitations with refining, these models in sepsis identification could transform current clinical practice with improved clinical outcomes. Deep learning Machine learning Detection Sepsis Neural Network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Sepsis is a potentially lethal situation induced by the body's reaction to infection and continues to be one of the great global challenges, most dramatically seen in developing nations [1]. Sepsis is among the leading causes of death in intensive care units (ICUs) worldwide and its recognition, particularly in the early stages of the disease, remains a medical challenge. The advent of an affluence of available digital health data has created a setting in which machine learning can be used for digital biomarker discovery, with the ultimate goal to advance the early recognition of sepsis [2]. The synergism between constrained healthcare resources, disparities in the economy, and weak infrastructure increases the incidence of sepsis [3]. In developing countries, the main barriers to timely detection and effective management of sepsis include limited access to healthcare services, lack of essential medical supplies, and insufficient healthcare infrastructure. Socioeconomic factors such as poverty and malnutrition also increase susceptibility to infections, thus amplifying the risk of developing sepsis. Addressing these challenges is important not only in reducing illness and mortality due to sepsis [4, 5]. Sepsis is still a leading cause of mortality in the critically ill patient, especially in the intensive care unit and emergency department. Early detection and timely intervention improve the outcome of the patient. The application of ML models is becoming a promising tool for enhancing the prediction and diagnosis of sepsis through clinical data and advanced algorithms for identifying high-risk patients. The research collection discusses diverse ML techniques: from traditional models, such as Random Forest and Logistic Regression, to more complex approaches, like Convolutional Neural Networks and ensemble methods. It demonstrates potential for improving detection of sepsis, prediction of mortality, and clinical decision-making. 1.1 Importance of early diagnosis Early diagnosis of sepsis is important because the condition progresses very rapidly in a medical emergency. If untreated, it leads to failure of the organs and subsequently death. It is brought about by an inappropriate immune response to an infection, with the associated massive inflammation and tissue destruction, potentially escalating into septic shock-a life-threatening condition, with severely low blood pressure and multi-organ failure [6]. Timely detection enables healthcare providers to administer life-saving interventions such as antibiotics, fluid resuscitation, and supportive therapies, significantly improving patient outcomes. The research interest among different stakeholders in sepsis reveals the collaborative approach to fighting this life-threatening condition. Institutions account for the highest percentage at 36%, with a focus on furthering theoretical understanding and finding new solutions. Clinical trials amount to 21% in translating research into practice, testing treatments and interventions. Pharmaceutical companies contribute 15% in developing therapies for sepsis-related complications. Laboratory studies, at 13%, deliver critical perceptions into the biological mechanisms of the disease, running embattled research. Patient advocacy groups, making up 9%, raise awareness, influence research priorities, and ensure patient-centered approaches, while policymakers, comprising 7%, shape healthcare policies, funding, and regulatory frameworks to support sepsis management. This distribution in Figure 1 below reflects a multidimensional effort to combat sepsis, aligning with the challenges and opportunities presented earlier. The involvement of diverse stakeholders, combined with advancements such as machine learning and digital biomarkers, is crucial for addressing the complex nature of sepsis and improving patient outcomes. 1.2 Challenges in Sepsis Diagnosis Studies indicate that each hour of delay in treatment increases mortality risk by 7-8%, highlighting the time-sensitive nature of sepsis. Unlike many other diseases, its non-specific symptoms, such as fever and rapid heart rate, make early recognition challenging, yet essential, to prevent catastrophic outcomes and save lives [7, 8]. One major challenge with sepsis is that its underlying processes are not comparable to conditions like inflammation. These conditions share indicators such as altered blood pressure and heart rate, symptoms like fever, and consistent molecular patterns, including immune system imbalance. Due to the unique systemic characteristics of sepsis, the use of biological and molecular indicators (commonly referred to as biomarkers) has been proposed as a potential approach to improve the accuracy of sepsis diagnosis and identification [9, 10]. Although significant efforts have been made to identify suitable indicators, the low sensitivity and specificity have limited the broad acceptance of any single or combined biomarker for diagnosing and treating sepsis [11, 12]. This is despite the many efforts that have been undertaken. The development of data-driven biomarkers has seen significant progress in recent decades and holds promise for addressing current difficulties. The method aims to analyze and use health-related information (such as patient records or medical data) by using machine learning techniques that identify patterns and make predictions or decisions based on data. The availability of digital data at high resolution is progressively expanding, catering to those at risk of sepsis and those already suffering from sepsis [13]. The dataset encompasses a comprehensive range of information, including laboratory results, vital signs, genetic data, molecular data, clinical records, and health history. A comprehensive study of digital health markers enabled by flexible data use could replace the traditional narrow focus on evaluating only blood test indicators for health insights. Machine learning algorithms offer a natural ability to effectively analyze complex and diverse digital patient data to accurately predict the development of sepsis in patients [14, 15]. This is achieved by identifying and using predictive patterns in the data. This allows us to accurately predict who is likely to develop sepsis. The identification of predictive patterns is usually performed using one of two approaches: Supervised Machine Learning or Unsupervised. Supervised learning algorithms are typically used to predict unlabeled data using labeled training data (e.g., whether a patient has sepsis or not) as a source of knowledge. In contrast, unsupervised learning does not involve the process of assigning labels to data. These algorithms are responsible for searching for patterns in the data, both familiar and unfamiliar. Many studies published in the past two decades have used various computer models to effectively predict the development of sepsis at the early detection stage [16, 18]. A proposed detailed framework was introduced called the multi-task Gaussian process recurrent neural network (MGP-RNN), which could be used for end-to-end training and handling of multiple tasks. Several alternative approaches based on a combination of multi-task Gaussian processes and recurrent neural networks for the proposed system have been discussed. These are methods that indicate very accurate predictions for the onset of sepsis. As such, for instance, antibiotics were prescribed 17 hours late, and sepsis only 36 hours later [19]. In their paper [20], the authors further proposed that single-task Gaussian processes can be integrated into neural networks. They also introduced the concept of a "Gaussian process adapter," which can be effectively applied within the end-to-end learning paradigm. In an earlier study, the substitution of the last fully connected layer with a Gaussian process adapter architecture that includes the usage of dynamic time warping along with a TCN termed MGP-TCN leads to a superior prediction of early detection of sepsis with much higher accuracy [21]. Considering such massive research in the direction along with its huge day-to-day improvement, it's significant to time and again realize the new state-of-the-art improvements done in this area as well [22]. This review shall outline the current state-of-the-art machine learning models for identifying potential digital biomarkers in the early detection of sepsis in an ICU. The study aims at filling in the gaps between conventional diagnostic methods and discovering innovative ways to improve outcomes. The key contributions to this review are listed as follows: Systematic Literature Review and Meta-Analysis: A demanding systematic review with meta-analysis is considered for the identification, critical appraisal, and synthesis of all the available evidence surrounding the early detection of sepsis through machine learning model applications. This involved appropriate, extensive searching of relevant databases using strict inclusion and exclusion criteria and critically appraising the included studies. Comprehensive Analysis of Machine Learning Techniques: This review presents an in-depth analysis of various techniques of machine learning that have been applied in the field of sepsis detection, including traditional algorithms (decision trees, support vector machines) and advanced techniques, such as deep learning, and ensemble methods. Evaluation of Model Performance: It examines the performance of a machine learning model in terms of sensitivity, specificity, and accuracy. Other relevant metrics to discuss are those that make model performance. These would include data quality, feature engineering, and model selection, among others. Identification of Key Challenges and Limitations: The review identifies important limitations and challenges related to using machine learning for sepsis detection, such as low-quality data, low model interpretability, and limited ethical considerations. Exploration of Future Research Directions: The review outlines promising avenues for future research that shall help address the challenges identified for the improvement of accuracy and reliability in machine-learning-based models for sepsis detection. These directions incorporate explainable AI models, multi-modal data integration, and advanced techniques for feature engineering. Clinical Implications and Recommendations: It gives potential clinical implications on machine learning for early sepsis detection and proposes future recommendations for research and practice in the clinical sector. By providing a clear overview of the current landscape and future possibilities, this review aims to advance early sepsis detection and improve patient outcomes. 1.3 Research Questions The following research questions, which are derived from the stated objectives, need to be addressed to thoroughly investigate and achieve the goals outlined in the study, providing a clear direction for the research process. RQ1: Which machine and deep learning algorithms have been used for sepsis prediction? RQ2: How do the algorithms differ concerning predictive accuracy, data requirements, and clinical application? RQ3: How do various machine and deep learning algorithms compare in predicting sepsis regarding accuracy, methodology, and performance metrics across different datasets? RQ3: What strategies have been adopted in the existing research to navigate the ethical and data privacy challenges posed by applying machine and deep learning to sepsis prediction using healthcare data? 2. Systematic Review Procedure 2.1 Search Strategy A multi-pronged approach was employed to ensure a comprehensive exploration of the topic. Initially, a thorough search was conducted across several esteemed databases, including IEEE Xplore, ACM Digital Library, and Scopus. These databases are renowned for their vast collection of articles spanning biomedical, technical, and interdisciplinary research domains. Table 1 below presents the search results obtained from these databases, highlighting the number of articles retrieved from each: 310 from IEEE, 206 from ACM, and 1,011 from Scopus. The search results from 3 different databases are as follows in figure 2 below, The efficiency of the literature search was also increased by identifying the Boolean operators to be used. These operators, especially “AND” and “OR,” made it easier to combine relevant search terms while isolating irrelevant articles [23, 24]. For instance, the following sources were generated: “Sepsis” joined with “Machine Learning” using the “AND” operator, avoiding articles that merely discuss the utilization of various machine learning approaches in the identification and diagnosis of sepsis. Furthermore, filters were applied to increase the degree of specificity in line with certain integrated parameters such as the kind of articles, date of publication, and language of the articles [25]. Due to the need to ensure the articles included in this review are as up-to-date as possible, a clear timeline by which the literature must have been published was set. The review has restricted its focus to articles published in the last two decades, outlining recent developments and trends in the field. This proposed strategy corresponds to methodologies used in literature reviews, where delimiting a specific time range has proven effective in maintaining the review’s relevance. Thus, the search strategy that has been put in place for this review has been designed to be comprehensive while also being thorough. By using the mean of highly recognized databases, application of the Boolean operators, and search filters as well as setting a clear temporal constraint for the inclusion, the aim of setting up the literature Scopus in Figure 3 was to capture the vast and diversified published literature in the field of predicting sepsis through machine and deep learning techniques. 2.2 Study Selection Criteria The process of selecting studies for a systematic literature review requires careful attention to ensure both relevance and completeness. This review relied on clearly defined inclusion and exclusion criteria, which played a key role in choosing the articles. For the inclusion criteria, preference was given to significant research studies that explored sepsis prediction using structured and thorough methods. These studies primarily focused on human participants to provide a better understanding of the real-world effectiveness of these algorithms. For example, the paper by [28] focuses on the application of a machine-learning framework for in-hospital mortality prediction for ICU patients with sepsis, demonstrating the potential and flexibility of machine learning in this domain. But still, it was important for the review to be fair and relevant. Thus, for these reasons, several sources were excluded. For language considerations, articles in non-English languages were omitted; they may be challenging to interpret and, in turn, affect its accuracy. Further, studies that did not involve machine learning or deep learning directly in the context of sepsis prediction, or studies that were not focused on human subjects, were excluded. For instance, research using a machine learning-based radiomics model in predicting HPV types in cervical cancer [31] provided relevant information but it was excluded since it does not focus on the interest of the review. 2.2.1 Screening Process The screening process was structured in a step-by-step manner. Titles and abstracts were screened first to filter out irrelevant studies. Then, full-text articles were critically assessed for eligibility based on the criteria. To ensure that the study selection process is reliable and consistent, an inter-rater reliability approach was adopted [26]. Inter-rater reliability corresponds to the agreement at which multiple reviewers independently review, and classify the same data, or studies. Two separate independent reviewers independently evaluated two independent reviewers for study inclusion eligibility using predefined criteria in the current review. To reduce bias and accuracy, an agreement between reviewers regarding exclusion/inclusion was sought by taking recourse towards a third reviewer for final resolving the discrepancies. It will minimize the chances of lowering reliability during the selection of the study. This approach ensures that the selection is both thorough and well-organized, combining structure with teamwork to maintain rigor and completeness [27, 28]. The study selection criteria, planned and guided by a structured methodology, ensured the inclusion of high-quality. The study is relevant to research on sepsis prediction using machine and deep learning techniques. The criteria were designed to capture well-conducted studies on the clinical application of those algorithms. Each stage, beginning with defining criteria to the final assessments, was carried out maintaining accuracy, consistency, and relevance. This was done in two steps: the titles and abstracts were reviewed first to exclude irrelevant studies, followed by a detailed full-text assessment to confirm eligibility. This ensured that only relevant and high-quality studies were included. 2.2.2. Inclusion Criteria IC1: Studies using clinical trials and datasets for the validation of the predictive models in sepsis outcomes. IC2: Studies that have applied machine and deep learning techniques for sepsis prediction with human subjects. IC3: Studies that are well-structured and have clinical applicability IC4: Studies that were published between 2016 and 2025. 2.2.3 Exclusion Criteria EC1: Studies that do not use structured methodologies and are not based on clinical trials or observational data related to sepsis prediction. EC2: Studies that are not conducted to practice or do not demonstrate in practice the effectiveness of sepsis prediction models. EC3: Research that was not specifically conducted on the prediction of sepsis by the machine or deep learning. EC4: Researchers without human subjects and were not related to sepsis prediction. 2.2.4 Selection Process The selection process for the SLR started with obtaining articles from three important databases: IEEE, ACM, and Scopus. In total, 310 articles were retrieved using IEEE, 206 using ACM, and 1011 using Scopus. Duplicates are identified and extracted from the dataset, hence removing 600 entries. Articles in Chinese (75) and French (70) have been excluded due to restrictions on a language. After removing duplicates and language-specific articles, there were 782 articles remaining for screening. Articles from the screening stage that were out of date or outdated in their technique were excluded at this stage. 445 articles were removed from the pool of selections at this step. Then, 237 articles were requested to retrieve full texts, and a request was made to retrieve all those articles to examine further. However, 162 could not be retrieved, and the remaining 125 articles were forwarded for eligibility. The remaining articles were assessed for eligibility at the eligibility assessment stage. Of the 125 articles that were evaluated, 45 were excluded. The reasons for exclusion include review article 27, AURAC absent in studies 8, meta-analysis 5, conference paper 3, and book chapter 2. Following these exclusions, a total of 80 studies remained to be included in the final SLR. This detailed process ensured that only relevant and of high quality, meeting the necessary criteria studies were included in the review. The process of selecting relevant research papers for the study is illustrated in Figure 4, which presents a visual representation of the overall steps involved in the selection process. It outlines the key stages for identifying and choosing the most pertinent papers for the study. 2.3 Distribution of Studies Across Years Of the reviewed studies, the temporal analysis indicates that interest in this research area remains constant from the observation period of 2016 to 2025. This distribution also reflects the concern for artificial intelligence in the prognosis of sepsis and the increase in publications on this aspect because of acknowledging sepsis as a major global health concern [93]. The number of selected papers over the years is illustrated in Figure 5 reflects that the count of selected papers was a little higher in the year 2020. 2.4 Quality Assessment The following criteria were selected to perform the quality assessment and relevance of the selected articles. Data Preprocessed: Assess whether the dataset underwent cleaning, transformation, and normalization to ensure quality and usability. Data Source and Collection: Examines the origin and methodology used for gathering the data, ensuring reliability and relevance. Source of Feature: Evaluate whether the features (variables) used in the analysis are well-defined and appropriate for the study objectives. Ethical Issue: Investigates adherence to ethical guidelines, such as data privacy, consent, and responsible usage. Detail Discussion: Check if the study includes comprehensive explanations and critical analysis of the findings. Measurement of Model’s Performance: Reviews the metrics used (e.g., accuracy, sensitivity) to assess the effectiveness of the proposed model. Evaluation Method: Analyze the robustness of validation techniques like cross-validation or independent testing for model performance. This assessment focused on several key aspects, including data quality, preprocessing techniques, feature engineering, ethical considerations, model evaluation, and the level of detail in the discussion. Each study was evaluated against these criteria, and the results are summarized in the table below. A score of "1" indicates that the criterion was met, while "0" indicates that it was not. By systematically evaluating these factors, we aimed to identify studies that adhered to sound methodological practices and provided robust evidence to support their findings. Table 1 highlights the quality of the included studies. “0” is marked as not available/not present and “1” is marked as available or present. 2.5 Experimental Design Choices for Sepsis Onset Prediction To ensure the robustness, applicability, and relevance of the findings in real-world sepsis prediction, it is essential to evaluate the study's methodological approach, data diversity, and the clinical utility of machine learning models. Following are the types of designs and techniques used as experimental designs for sepsis prediction are mentioned in Figure 6. Retrospective vs. prospective: Most studies used retrospective approaches to analyze past patient data. However, they provide rich datasets but cannot capture the ever-changing nature of clinical practice. Future designs that collect data in real-time will provide insights into how the models perform in real-world settings. Single-center vs. multicenter: Single-center approach extracts data from a single center, which limits the generalizability of the results. In contrast, multicenter studies improve the robustness of the model by including different datasets. Prediction time window: Refers to how far in advance the machine learning model predicts sepsis relative to its diagnosis. This approach influences both the model's accuracy and its practical utility in clinical settings, highlighting the importance of early detection for timely intervention. Various experimental design choices are employed in sepsis onset prediction in Table 2, which outlines key factors that influence the development and effectiveness of predictive models. 2.6 Data Sources for Sepsis Detection The features and data sources employed in sepsis detection using machine learning and deep learning models are diverse and extensive, reflecting the multilayered nature of the condition. The careful curation of relevant data points and reliance on comprehensive, accurate, and real-time data sources is imperative in the development of models that are not only high-performing but also clinically relevant and conducive to integration into existing healthcare systems [99]. The primary sources of data for these studies largely include. Electronic Health Records (EHRs): EHRs are a digital version of a patient’s paper chart and are a rich source of patient history, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, and laboratory test results. Many models leverage these data repositories due to the comprehensive nature of the patient data they contain. Vital Signs Monitors: Real-time data from monitors capturing patients' vital signs are particularly valuable for models focusing on early detection of sepsis, where timely intervention is critical. Databases and Health Registries: Some studies also utilize large-scale health databases and registries that collect data from various healthcare settings for a more population-based analysis. The extracted data was organized to facilitate analysis and comparison, with Table 2 summarizing key data points. These include study design, which refers to the type of research structure such as prospective, retrospective, or experimental. Data sources are the origin of the data such as electronic health records, ICU databases, or simulated data. Sample size indicates the number of participants or data points analyzed. The quality tier assesses the study’s methodological rigor and reliability. These elements bring out trends and challenges in the field. Diverse datasets and rigorous analysis are underlined to boost prediction accuracy and guide further research directions. 3. Overview of ML, DL Models, and Their Features for Sepsis Prediction The role of ML and DL is central to predictive technologies in healthcare, especially with sepsis detection. There has been an impressive array of computational methods from decision trees to neural networks, which have been effectively applied to develop early warning systems for sepsis. These models rely on several critical features, including physiological variables, laboratory results, and demographic data, that are essential for accurate prediction. This shows the potential of a novel, more effective approach toward sepsis prognosis and underlines the role that data-driven models play in improving patient outcomes by integrating diverse features with ML and DL models [91, 92]. 3.1 Types of Models Used The section describes different variants of ML/DL models that have been proposed to determine and predict cases of sepsis based on their applications and are crucial for resolving the complexities regarding the processing of clinical data. There are quite popular techniques, namely Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, Gradient Boosting Machines, Neural Network methods, and ensemble or DL variants. Each of these methods contributes uniquely toward overcoming challenges in analyzing complex clinical data for early and accurate sepsis diagnosis, thus demonstrating the range of computational approaches that are under consideration in this field. 3.1.1 Decision Trees and Random Forests The simple yet powerful model is referred to as a decision trees that function similarly to flowcharts: At each node, the features split the data, so the prediction happens. Particularly effective at dealing with data of both categories and types, they can be highly used in the analysis of clinical datasets for sepsis detection. However, they may overfit small datasets, thus being less generalizable. This limitation is addressed by Random Forests, which is an ensemble of Decision Trees, by aggregating predictions from multiple trees, thus reducing the overfitting and improving accuracy. This method is particularly useful in capturing nonlinear relationships in complex sepsis indicators and is widely applied in the analysis of physiological and clinical data. 3.1.2 Support Vector Machines (SVMs) Support Vector Machines are robust supervised learning models and can be very good with high-dimensional spaces. It finds the best hyperplane that separates data points into different classes. In nonlinear cases, the kernel function such as the radial basis function is used to project the data to higher dimensions. SVMs are especially useful in datasets having large numbers of variables. These include physiological and biochemical markers used for the detection of sepsis. SVMs are computationally expensive, and in some cases, they become computationally expensive for handling large amounts of data and hyperparameters which require careful tuning. 3.1.3 Logistic Regression Logistic Regression is a statistical model for binary classification, used in tasks such as predicting the presence or absence of sepsis. It works on estimating probabilities through a logistic function and is valued for its simplicity and interpretability. The model is excellent when the relationships in the data are linear and, hence, it is a popular choice for the initial prognosis of sepsis. However, in reality, it fails to identify complex interactions between the variables, which limits its application in more complicated clinical datasets. Still, it acts as a reliable baseline model for sepsis prediction studies. 3.1.4 Gradient Boosting Machines (GBM) Gradient Boosting Machines are advanced ensemble methods where the decision trees are learned sequentially. The new tree learns the mistakes of the previous tree, and this process is continued for several iterations. Popular variants include XGBoost, LightGBM, and CatBoost. They can handle missing data, thus making them highly accurate for predictions. This type of model works very well for datasets involving clinical data with several features such as laboratory results and vital signs. While Gradient Boosting Machines are very accurate, they require careful regularization to avoid overfitting and are computationally very intensive for very large datasets. 3.1.5 Neural Networks Neural Networks, a brain-inspired structure, are well suited for complex, high-dimensional datasets. CNNs are used widely to capture spatial patterns in data, such as time-series vital signs, while RNNs are well suited for handling sequential data and thus are better suited for tracking the progression of sepsis. Such models rely on layers of interconnected neurons to learn the intricate relationships within the data. However, they are often computationally intensive, need larger datasets for training, and are criticized for a lack of interpretability, which sometimes will not support clinical application. 3.1.6 Ensemble Methods Ensemble methods combine the strengths of multiple machine learning models to improve overall prediction accuracy and stability. Some techniques applied in sepsis detection include bagging, boosting, and stacking. Models like Random Forest, Gradient Boosting, and Logistic Regression can be combined for better robustness and bias or variance reduction. Ensemble methods are especially useful in clinical environments where reliability is essential. These methods are computationally expensive and less interpretable than a single model despite their accuracy. 3.1.7 Deep Learning Models Deep learning models, such as DBNs and Autoencoders, provide powerful means of extracting complex patterns in large-scale clinical data. DBNs are stacked restricted Boltzmann machines; they are mainly used for pretraining in an unsupervised manner before engaging in a task that requires supervision. The autoencoders are successful in terms of dimension reduction and feature extraction, with the help of which high-dimensional data patterns can be picked up and identified. These models are especially useful in dealing with heterogeneous and multi-dimensional datasets related to sepsis. However, they do require a lot of computing power and tend to be considered "black-box" models, which may affect their adoption in clinical environments. 3.1.8 SHAP (Shapley Additive Explanations) Although these methods are accurate, they are computationally expensive and less interpretable than single models. It's a model-agnostic explainable tool that uses SHAP, which attributes the contribution of each feature to the outcome. It solves the issue of interpretability with complex ML models by giving a more qualitative interpretation of the influences on prediction from individual variables such as vital signs or biomarkers. SHAP enhances transparency and clinician trust and would be a valuable addition to sepsis prediction frameworks. However, it can be computationally expensive when applied to large datasets with numerous features. These models, each with unique strengths and limitations, contribute significantly to advancing sepsis detection and prediction, enabling more accurate diagnoses and timely interventions to improve patient outcomes. For instance, Decision Trees (including Random Forests) are favored for their interpretability and ability to handle nonlinear relationships, crucial for identifying complex sepsis indicators [83, 37]. Support Vector Machines (SVMs) prove effective in high-dimensional spaces, making them beneficial for datasets with numerous parameters [85, 86]. Logistic Regression offers a statistical approach to predicting binary outcomes, making it suitable for initial sepsis prognosis [87]. Gradient Boosting Machines are capable of handling missing data and improving prediction accuracy through ensemble learning [88, 67]. Neural Networks (CNN & RNN) are employed for their ability to capture temporal patterns in patient data, vital for tracking sepsis progression [89, 35]. Some studies also showed a trend towards using Ensemble Methods, which combine multiple machine learning models to improve prediction accuracy and stability, essential for reliable sepsis detection in clinical settings [90]. In addition, deep learning (a subset of machine learning) has become increasingly popular, with models such as deep belief networks (DBNs) and autoencoders excelling at extracting complex representations from multifaceted sepsis-related data [91]. This points out the continuous development and application of sophisticated computational tools to improve sepsis diagnosis and prediction. Tremendous diversity and growth have been evident in the research field utilizing machine learning and deep learning for sepsis prediction. The gradual increase of the range of models employed and the research output during the last years has confirmed the relevance and potential of such advanced computational techniques to enhance the prognosis and management of sepsis, and thus enhance clinical decision-making, as well as to reduce the morbidity and mortality due to this disease. [100, 101]. 3.2 Features and Variables Utilized in Sepsis Detection This subsection represents the specific features and variables used in the included studies for sepsis detection and prediction. The quality and specificity of the data sent to the healthcare field are significantly dependent on the effective application of machine learning and deep learning models in healthcare. In the context of sepsis prediction, the selection of relevant features and reliable data sources is critical to developing accurate, reliable, and clinically applicable prediction models [96, 97]. The reviews undertaken have suggested the use of features and variables, showing that sepsis is a complex, multi-factorial process. Features in general can be classified under these headings: Physiological variables: These are such features as heart rate, respiratory rate, body temperature, blood pressure, and oxygen saturation. Laboratory findings: General laboratory findings include leucocyte count, thrombocyte count, creatinine, bilirubin, lactate, and blood gas, among others Clinical findings: History of previous medical conditions, drug intake, invasive devices in place, and documented signs of infection and systemic inflammatory response syndrome. Demographic factors: Age, sex, ethnic group, as these parameters affect the risk of development of sepsis as well as outcomes. To understand the methodologies and feature engineering techniques that have been used in the different studies on sepsis prediction, we have provided an overview of the machine learning models used in the studies. The following table 3. Breaks down these models in detail and the features considered in each study. Table 3: Sample Table for Machine Learning Models and Feature Sets for Sepsis Prediction References Model Used Features and Variables [27] Random Forest Physiological variables, Laboratory results, Clinical data, Demographic information [28] Blending model baes on Random Forest (RF), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Neural Networks (NN), Decision Trees (DT), Gradient Boosting Machines (GBM) Physiological variables, Laboratory results, Clinical data, Demographic information [29] Random Forest Physiological variables, Laboratory results, Clinical data, Demographic information [31] Support Vector Machine (SVM), Logistic Regression (LR) Clinical data, Demographic information [32] Linear Regression, Neural Networks Clinical data, Demographic information [33] Regression Algorithms Laboratory results [34] XAutoNet, Convolutional Neural Network (CNN)-based Autoencoder Demographic information [35] XGBoost, Light GBM, Random Forest Physiological variables, Laboratory results, Clinical data, Demographic information [36] Deep learning model: Multi-output Gaussian Process and Recurrent Neural Network (MGP-RNN). Physiological variables, Laboratory results, Clinical data, Demographic information [37] Convolutional Neural Network (CNN) and Random Forest (RF) Physiological variables, Laboratory results, Clinical data, Demographic information [38] Ensemble of Support Vector Machine, Random Forest, Naïve Bayes, Logistic Regression, and Xtreme Gradient Boost. Physiological variables, Laboratory results, Clinical data, Demographic information [52] logistic Regression, Random Forest, or Support Vector Machine (SVM). Physiological variables, Laboratory results, Clinical data, Demographic information [53] eXtreme Gradient Boosting (XGBoost) Physiological variables, Laboratory results, Clinical data, Demographic information [58] LASSO, Random Forest (RF), Gradient Boosting Machine (GBM), and Logistic Regression (LR). Physiological variables, Laboratory results, Clinical data, Demographic information [62] Convolutional neural networks Physiological variables, Laboratory results, Clinical data, Demographic information [67] logistic regression, naive Bayes, SVM, KNN, Gaussian process, random forest, AdaBoost, gradient boosting Physiological variables, Laboratory results, Clinical data, Demographic information [73] Lasso Regression, SHAP, ML methods (XG Boost, Random Forest, Naive Bayes, Logistic Regression, SVM, KNN, Decision Tree) Physiological variables, Laboratory results, Clinical data, Demographic information [75] Deep Learning Model: COMPOSER Physiological variables, Laboratory results, Clinical data, Demographic information [84] eXtreme Gradient Boosting (XGBoost) Physiological variables, Laboratory results, Clinical data, Demographic information [88] Gradient Boosting Physiological variables, Laboratory results, Clinical data, Demographic information Figure 7 depicts the number of research focuses on different categories of features. The x-axis is "Features," "Clinical Data," "Demographic Info," "Laboratory Results," "Physiological Variables," and "Others." The y-axis represents the number of papers concentrating on each category. It can be seen from the graph that "Physiological Variables" received the maximum attention with eight papers, and the second maximum was for "Clinical Data" with four. The papers for "Laboratory Results" and "Demographic Info" were relatively low in numbers. This visualization gives a summary view of the research trend in the specific dataset or study. Features and data sources for use in sepsis detection models based on machine learning and deep learning models differ, as the nature of this condition is complex with a multi-layered appearance. Careful selection and incorporation of relevant data points plus the use of comprehensive, timely, and accurate data sources for models that are not only performance-enhancing but also clinically significant and easily integrated into ongoing health systems [99]. 3.3 Sepsis Detection and prediction techniques The detection and prediction of sepsis have greatly evolved with the adoption of machine-learning techniques. These methods incorporate a wide range of approaches, including data integration, real-time applications, and robust evaluation metrics, for improving diagnostic precision and timely interventions. A technique in this context refers to a specific methodological approach or computational strategy applied to tackle distinct challenges in sepsis detection. The aim of using such methods is to enhance early diagnosis, ensure the reliability of the model, integrate multiple sources of data, and provide actionable insights to clinicians, ultimately enhancing patient care and clinical outcomes. Following are the comprehensive ten key techniques employed in sepsis detection and prediction, along with their associated references and detailed descriptions. 3.3.1 Highlighting Heterogeneity in Approaches Sepsis presents diverse clinical manifestations, influenced by factors such as age, infection types, and comorbidities. Predictive tools like SHAP (SHapley Additive exPlanations) facilitate the identification of key features impacting outcomes, such as lactate levels and inflammatory markers [32,73]. Additionally, incorporating patient-reported data addresses variability, enabling more tailored and accurate risk assessments. 3.3.2 Comparative Evaluation of Models The complexity of sepsis often surpasses the capabilities of single predictive models. Ensemble models, like Random Forests and Gradient Boosting Machines, combine multiple algorithms to improve performance and reliability [28, 29]. These methods effectively capture non-linear relationships and interactions among sepsis biomarkers, outperforming simpler models. 3.3.3 Integration of Data Types Integrating structured data (lab findings, vital signs) and unstructured data (clinical notes, imaging scans) improves the predictive model. Models created using information from many types of data stand a better chance of predicting the risk of developing sepsis, or its worsening [36, 75]. This integration in its entirety depicts patients’ conditions from multiple perspectives, thus aiding in enhanced decisions at the clinical level. 3.3.4 Application to Specific Clinical Scenarios Personalized predictive models for specific populations are more applicable and accurate. For example, models of sepsis in neonatal babies revolve around different physiological constants whereas models for sepsis in immunocompromised patients consider different parameters [36, 67]. Custom-made algorithms enable more specific and effective targets for the interventions. 3.3.5 Temporal and Internal Validation External validation and temporal validation are important in building consistent and applicable models. Testing for sepsis should include assessment based on the stage of the disease starting from the first diagnosis to the last stage. Validation done in time makes it clear that models would be statistics refraining from changing places with time, irrespective of the advancement in clinical settings [35, 53]. 3.3.6 Performance Metrics for Evaluation Some key performance indicators such as AUC (area under the curve), sensitivity, and accuracy have a very crucial role in the determination of model use performance [27, 88]. When sensitivity is high, the risk of obtaining a false negative score is very small, thereby ensuring. 3.3.7 Timeliness and Real-Time Applications Sealth care organizations' tools for quick detection and diagnosis are fundamental to the management of sealer infection. Predictive algorithms embedded in hospital information systems make up alerts that give patients an ultimatum based on how their vitals are performing so that hints on treatment decisions can be made promptly [33,75]. Such technologies do drastically cut down the lengths of time it takes seeking interventions improving the chances of survival of the patient. 3.3.8 Experimental Evidence Presentation The experimental proof shows the possibilities of the sepsis prediction models. Tough testing on patient databases improves the strings of detection being early and timely, predictive efficacy, and dependable models [28, 35]. Seeing that they can perform equally well in multiple diverse populations builds great trust in these advanced models. 3.3.9 Economic and Clinical Impact Assessment If the sepsis model is at its best, then hospitals would be best to follow it as it could help reduce overall hospital costs as well as make the best use of living by reducing serious key entries for injured patients [39,75]. Models put in place will also enable patients to adhere to clinical procedures increase death rates and decrease the number of burdens for treating patients. Measuring those effects will support the need for specific technologies for prediction purposes in healthcare systems. 3.3.10 Tabular and Visual Summarization Graphical representations such as heatmaps and time-series graphs also facilitate the interpretation of finer details of sepsis data overload. Clinicians can pinpoint. 3.4. Evaluating ML-Driven Solutions and Practical Advancements in Sepsis Prediction This section evaluates the impact of machine learning (ML) technologies on sepsis detection and prediction in light of clinical needs. By providing information from multiple sources such as EMR, vital signs, and demographic data, it has been shown that ML models have great potential in achieving sepsis predictions in a very timely manner and with great accuracy. For instance, the ‘NAVOY Sepsis’ prediction algorithm which is developed and externally validated seeks to predict the chance of a patient developing Sepsis in ICU which proves its efficacy in being a predictive model for early treatment. Likewise, a model used in emergency departments was superior in the accuracy of predicting sepsis detection than current early warning score systems [42-44]. These advances serve to illustrate how ML methods can be useful for revolutionizing the detection of sepsis in terms of the time and accuracy of the predictions. In addition, it has been recommended to use a multi-approach to improve the incorporation and use of ML for early sepsis detection [45]. Another significant advancement concerns an ensemble model that did not only combine LightGBM, XGBoost, and Random Forest but also relied on hourly patient data to surpass other tree models in terms of predictive performance [46]. These examples combined confirm that there are real-world improvements and usefulness of ML-based systems in the clinical management of sepsis patients. 3.5. Feature Engineering for Sepsis Detection To be effective in predictive modeling, it is very important to emphasize feature engineering or a corresponding task that would include the transformation of the raw data into more useful features. For example, concerning sepsis detection, feature engineering involves defining and querying the specific clinical features from structured clinical databases such as EHR, laboratory results, and vital signs. By targeting the best predictors even at the early stage of model building such as placing the most effective features, it is possible to increase the efficiency of the models aimed at predicting the occurrence of sepsis. The model-building process also entails dealing with issues like data absence, overfitting of the model, and feature death to improve the model. This literature review discusses the state of the art of feature engineering as applied to sepsis detection including reviewing the most important studies on feature selection and feature extraction techniques for sepsis prediction. Following are the key points discussed in this review regarding the role of feature engineering in sepsis detection: 3.5.1. Feature Selection and Extraction for Sepsis Detection Feature selection and extraction play an important role in selecting the most relevant medical indicators for predicting sepsis. The studies stress selecting clinical input features that play a crucial role in determining the outcome of a patient and understanding the multifactorial causes of the syndrome. The proper extraction of these features increases the detection accuracy of sepsis [47, 49]. 3.5.2. Importance of Timely Sepsis Prediction The timely intervention of sepsis can be done with early prediction. Machine learning models like XGBoost have proved to be accurate predictors of sepsis within the first 6 hours of its onset. This suggests that machine learning and deep learning models must be implemented to ensure early detection and effective treatment [48, 50]. 3.5.3. Handling Missing Data and Overfitting Missing data handling and avoidance of overfitting play important roles in enhancing the precision and generalizability of a machine-learning model. The techniques used here, for feature replacement or selection are mean imputation and chi-square, which help maintain the robustness and reliability of the model while trying to predict sepsis [51]. 3.5.4. Model Interpretation Interpretability tools in machine learning, such as SHAP (SHapley Additive exPlanations) explain the reasoning behind certain predictions made by a model. SHAP helps reveal why a particular feature results in a model classifying a patient as sepsis. This adds explanatory power to models and guides clinical decision-making [52]. 3.5.5. Predictive Accuracy of Machine Learning Models XGBoost has demonstrated the highest accuracy of prediction in sepsis detection. Models like LSTM and SVM have shown their potential to be good in the detection of sepsis. These models work with high accuracy based on feature selection and extraction to enhance the accuracy of the predictions [53]. 3.5.6. Reducing Feature Redundancy Feature redundancy reduction is another efficient strategy to improve the performance of a model. Methods have been presented to remove redundant features from the data and extract strong, meaningful features from large dimensional data. This enables sepsis detection models to predict more accurately and reliably [56]. 3.5.7. Feature Engineering Across Models Feature engineering plays a vital role in different models for sepsis detection using machine learning. Choosing the correct features, extracting relevant data, and removing redundancy are the essential steps in creating an accurate model. Integration of all these strategies helps improve the performance in the prediction of the onset of sepsis [47, 49 and 53] 3.6. Overview of Previous Study Machine learning has brought about a revolution in healthcare in the prediction and management of sepsis, an extremely dangerous condition that necessitates immediate and accurate interventions. The studies are divided into five categories based on their objectives: early detection, mortality prediction, improved interpretability and feature selection, specialized clinical challenges, and evaluation of existing models. The early detection model focuses on models like Random Forest and deep learning methods that predict sepsis onset in the ICU for timely interventions. The study of mortality prediction applies models such as XGBoost and Random Forest for estimating hospital mortality risks and in clinical decision-making. It aims to enhance interpretability and feature selection, leveraging tools like SHAP with refined feature engineering to gain better performance. The specialized clinical challenges group focuses on ML applications in unique patient populations, for example, hematopoietic cell transplantation. Meanwhile, the evaluation frameworks group reviews existing models and tries to identify areas that could be improved. Thus, by categorizing these studies, we get a clearer view of the varied ways in which ML is developing sepsis prediction and management, providing insight for future research and real-world clinical applications. 3.6.1. Early Detection and Prediction of Sepsis in ICU This team of researchers is developing the early detection of sepsis using machine learning models in ICU settings. All the studies focus on high accuracy and early intervention. For example, one research [27] utilized the Random Forest model, resulting in an AUC of 0.94 to improve the prediction of sepsis mortality in patients within the ICU. Likewise, another research [34] used XAutoNet and CNN-based Autoencoder that showed public health benefits through early detection. In addition, the ensemble approach using XGBoost, Light GBM, and Random Forest was utilized [35] which provided improvements in accuracy to six hours before the diagnosis of a clinical disease. The MGP-RNN deep learning model was studied in a multi-center approach [36], and therefore, provided an excellent evaluation of early sepsis detection. Finally, the NAVOY Sepsis algorithm using CNNs yielded high AUROC scores in the early predictions for the European ICU environment [62]. 3.6.2. Mortality Prediction in Sepsis Patients This category focuses on estimating mortality in sepsis patients and sometimes applies biomarkers and clinical measurements to inform the selection of treatment. In this section, [28] created a new blending model composed of RF, XGBoost, LR, SVM, and more to learn interesting temporal correlations related to hospital mortality. Correspondingly, [58] took advantage of LASSO, RF, GBM, and Logistic Regression Models to predict ICU mortality related to patient risk by using metrics such as the Brier score and AUROC. Another work [84] obtained an AUC of 0.94 and an F1 score of 0.937 using XGBoost for in-hospital mortality prediction. Finally, [88] proposed a gradient-boosting model for emergency department triage, which boosted the classification accuracy to 97.67%, further optimizing resource allocation. 3.6.3. Improving ML Interpretability and Feature Selection Studies in this category involve making machine learning models interpretable and feature selection for improved performance. For instance, [53] applied XGBoost with SHAP analysis that provided interpretable results and outperformed other models such as MGP-RNN in prognosis prediction. Another study [52] used Logistic Regression, Random Forest, and SVM, combining modified chi-square feature selection to improve both recall and precision for the detection of early sepsis. A study [67] tested ML algorithms, such as Logistic Regression, SVM, and Random Forest, on various datasets and emphasized the feature importance and generalizability of models. 3.6.4. Advanced ML Techniques for Specialized Scenarios This group deals with advanced techniques of machine learning for particular clinical challenges. One article [31] applied SVM and Logistic Regression to predict the HPV type in cervical cancer patients; it had a very structured approach to extracting and analyzing data. Another study [37] was on CNNs and Random Forest models to predict outcomes of sepsis in patients undergoing hematopoietic cell transplantation. Moreover, [73] introduced SERA, an algorithm combining both structured and unstructured data that employs methods including Lasso, SHAP, and XGBoost to mitigate false positives with early detection of sepsis. 3.6.5. Evaluation Frameworks and Scoping Reviews These studies review and comment on existing machine learning models with insights and recommendations for further development. For example, [32] tested the ML models, which included Linear Regression and Neural Networks, in predicting neurodevelopmental outcomes, targeting quality improvement based on patient-reported data. Another related study [33] reviewed the regression-based ML model with a systematic approach that revealed the potential of unstructured clinical text in facilitating the early detection of sepsis. Finally, the COMPOSER deep learning model [75] was tested for its effect on clinical outcomes and resulted in a 1.9% reduction in mortality rates due to sepsis and improved compliance with care bundles for sepsis. This structured approach organizes studies based on their common purposes and contributions, giving me a clearer understanding of the contribution, they have to the subject of impacts on sepsis-related machine learning. The table below gives an overview of previous studies that have applied machine learning techniques to the prediction and management of sepsis. It groups the studies according to their objectives: early detection of sepsis, mortality prediction, model interpretability, and specialized clinical applications. By structuring these studies, the table provides an overview of the types of machine learning models used, including Random Forest, XGBoost, CNNs, and ensemble methods, as well as key findings and contributions from each study. This overview can be considered as a resource for understanding the current state of ML applications in sepsis care and helps identify trends, strengths, and areas to be explored further. 3.7 Background Li et al. [102] discussed the use of AI in sepsis care, particularly in the early detection and personalized treatment approaches. They also focused on the real-time monitoring of patient data to enhance clinical outcomes. Their review also emphasized the integration of predictive models into clinical workflows. The review study pointed out the challenges concerning adapting AI tools to different healthcare systems. The future directions include developing more interpretable AI models for clinicians. O'Reilly et al. [103] discussed optimizing AI tools in sepsis management, commenting on their utility for early diagnosis and treatment planning. The authors suggested multidisciplinary collaboration as the key to the effective use of AI. Identified barriers included algorithm bias and issues with data standardization. They provided an overview of the current state and proposed strategies for overcoming challenges associated with integrating AI. The main focus of their investigation was related to patient outcomes and possible AI contributions to these outcomes. Asif et al. [104] investigated machine learning progress in the field of medical diagnostics, specifically focusing on the potential to increase diagnostic accuracy. It explored several algorithms and their use cases in various fields of healthcare, including sepsis. Challenges such as a lack of available data and a requirement for high-confidence validation frameworks were identified. It pointed out that for successful implementation, collaboration between the clinician and AI is needed. Future studies should focus on ethical and technical issues. Yang et al. [105] recently reviewed the use of AI applications in the management of sepsis. There has been growth regarding the predictive accuracy of risks as well as decreased mortality with their application. Machine learning models discussed include those concerning clinical validation. High-quality datasets are crucial in training robust algorithms, and AI tool integration into healthcare systems represents a major challenge. According to them, the integration of AI could revolutionize sepsis management by improving patient outcomes. Rashid et al. [106] studied the use of gene expression-based machine-learning models to enhance the diagnosis and treatment of sepsis. They discussed genetic biomarkers as potential predictors with increased accuracy for better prediction. The authors further proposed the incorporation of multi-modal data to help in refining the decision-making process clinically. It stressed the idea of personalized AI-guided treatment plans. It discussed heterogeneity in data and interpretability as challenges. Gorecki et al. [107] highlighted the potential role of AI in the diagnosis of sepsis and septic shock, noting the ability to examine complex clinical data. A review was carried out regarding the performance of AI models across various healthcare settings. Data integration and standardization emerged as key barriers. Collaborative efforts by AI developers and clinicians were proposed to implement better systems. Future directions are focused on the fine-tuning of models to allow for real-time decision-making. Gao et al. [108] illustrated the utilization of machine learning in predicting ICU patient mortality by sepsis with clinical biomarkers. This research had promising high accuracy, but the work would be quite appropriate for a real-time environment. They indicated the need for much larger datasets in validating the model. Their challenges included the bias in algorithm and data preprocessing. Their findings did show a promise for an ML revolution in critical care. Padhi et al. [109] discussed AI applications in clinical virology, primarily on machine learning and deep learning. It explained how those technologies would be transformed to diagnose sepsis complications. The challenge in adopting AI was identified as data quality and model transparency. They proposed combining AI with molecular biology for better outcomes. The review also highlighted the necessity of interdisciplinary research. Hamid et al. [110] introduced a machine-learning model that was used for brain tumor detection from MRI images. The precision of the model was emphasized, although it had nothing to do with sepsis. They illustrated the diversity of AI in health care and touched upon difficulties related to preprocessing and scalability. Results indicated that developers of AI should collaborate more with clinicians. Further work needs to be conducted to improve the interpretability of models. Cheungpasitporn et al. [111] analyzed AI in the diagnosis of sepsis-related AKI and concluded that early detection is achievable through AI. AI can be employed for the interpretation of biomarkers for improved risk stratification. Challenges in integrating AI into the clinical workflow are a challenge, as well as issues of bias of the algorithm and lack of data standardization. Bignami et al. [112] provided an overview of AI applications in sepsis management for clinicians, noting advances in predictive modeling. Real-time analytics can be important in improving care for patients. The challenges identified include data privacy and the necessity of cross-institutional validation. The study further underlined the role of interdisciplinary collaboration in the refinement of AI systems. The future directions included integrating AI tools with electronic health records (EHRs). Wu et al. [113] compared logistic regression with machine learning for predicting mortality in adult sepsis patients. The authors showed that the machine learning model performed better when dealing with complex datasets. Srivastava et al. [114] discussed how AI could potentially be used early in the identification and treatment process of infectious diseases, such as sepsis. The authors acknowledged improvements in diagnosis through machine learning and deep learning-powered diagnostic aids. Issues that arose included standardized data and a lack of model generalization. The importance of real-time deployment of models was stressed in the study as well as emphasizing ethical AI design.Shayegh and Tadj [115] applied deep audio features along with self-supervised learning for early detection of neonatal sepsis. They proposed a model that analyzed infant cry signals to classify the diseases with great accuracy. However, they recommended that bigger datasets would enhance robustness. Other problems that are in their discussions were data variability and signal noise. Future work can be proposed for the integration of their model in neonatal care systems. Kombo et al. [116] suggested the development of an electronic nose using machine learning to diagnose neonatal sepsis from volatile organic compounds in fecal samples. It shows great potential in the early stages of the disease. The challenges presented include standardization of biomarkers and data acquisition. It emphasized integration into clinical settings as a step of prime importance. The areas of improvement involve the enhancement of sensor technology and sensitivity of the model [117]. Yeo et al. [118] explored the obesity paradox in sepsis and its long-term outcomes, underlining the differential effects of body composition. The study applied machine learning to patient data to look for trends. The diversity of data and lack of generalizability across populations were challenges [119]. Yong and Zhenzhou [120] applied deep learning for in-hospital mortality prediction for sepsis patients, with high predictive accuracy. Feature engineering was discussed in terms of its facilitative role in improving model performance. Challenges such as overfitting and a paucity of generalizability were posed. The importance of seeking AI integration into clinical workflows is emphasized in the study. Future directions encompass developing more robust validation frameworks. Boussina et al. [121] evaluated how deep learning-based sepsis prediction models affected the quality of care and survival. They showed importance in the aspects of earlier diagnosis and better patient outcomes. Challenges from real-time deployment and data standardization were reported. The authors also underlined the need for clinician involvement in developing AI. Scaling such models across healthcare systems should be considered for further research [122]. Pérez-Tome et al. [123] employed machine learning for the mortality of sepsis, demonstrating very high accuracy on clinical data. The research discussed feature selection and how this plays a major role in bettering the performance of the model. Difficulties, such as preprocessing of data, and ethical dilemmas, were examined. An urgent priority to the integration of AI tools in the clinical arena is indicated. Algorithms will need refinement for future use in real-time applications. Zhang et al. [124] conducted a multicenter study applying machine learning to predict in-hospital mortality in patients with sepsis. The authors demonstrated that the integration of clinical and inflammatory biomarkers results in higher predictive accuracy. Data heterogeneity, validation, and other issues related to this approach were discussed. The paper indicated the importance of cross-institutional collaborations in future work: generalizability of the models should be enhanced [125]. Table 4: Comparative Analysis of Sample Literature Ref. Title Objective ML Model Key Findings [27] Prediction of sepsis mortality in ICU patients using machine learning methods develop an ML model to improve sepsis prediction accuracy using a reduced set of features and enhance model interpretability. Random Forest XGBoost model achieved an AUC of 0.94 and an F1 score of 0.937 [28] Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis develop a novel blending ML model for predicting hospital mortality in ICU patients with sepsis, improving the score of SAPS II and SOFA Blending model based on RF, XGBoost, LR, SVM, k-NN, NB, NN, DT, GBM Insightful temporal associations in sepsis prediction [29] A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department identify diagnostic variables for sepsis at the ED using machine learning models trained with high-quality classifications assigned by experts. Random Forest Random Forest 97.55% sensitivity and 97.3% AUC [30] Performance analysis of class imbalance handling techniques for early sepsis prediction using machine learning algorithms To evaluate and compare three class imbalance handling techniques in medical datasets to improve machine learning-based sepsis prediction, and to develop a novel predictive model using only healthcare parameters available at peripheral basic health facilities (without requiring lab investigations). Sepsis Prediction Model for Peripheral Hospitals (SPMPH) The SPMPH model outperformed existing models, achieving: Accuracy: 0.95, Precision: 0.98, Recall: 0.91, AUROC: 0.978 [31] Prediction of carcinogenic human papillomavirus types in cervical cancer from multiparametric magnetic resonance images with machine learning-based radiomics models. Diagnostic and interventional radiology assess ML-based radiomics models for predicting carcinogenic HPV types from pre-treatment MRI features. Support Vector Machine (SVM), Logistic Regression (LR) Methodical approach in data extraction and analysis [32] Machine Learning Prediction Models for Neurodevelopmental Outcome After Preterm Birth: A Scoping Review and New Machine Learning Evaluation Framework review ML models for predicting neurodevelopmental outcomes in infants and assess quality for future improvements. Linear Regression, Neural Networks Innovative use of patient-reported data in sepsis prediction [33] Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review evaluate the impact of using unstructured clinical data in ML for the early detection, prediction, and identification of sepsis, compared to structured data. Regression Algorithms Limited by data scope but offers specific insights [34] Early prediction of sepsis using machine learning. In 2021 11th International Conference on Cloud Computing, Data Science & Engineering develop a classifier for early sepsis detection up to six hours before clinical diagnosis using patient data and evaluate ML models for accurate prediction. XAutoNet, Convolutional Neural Network (CNN)-based Autoencoder Broad public health implications in sepsis detection [35] An ensemble machine learning model for the early detection of sepsis from clinical data Establish an ensemble model for early identification of sepsis, 6 hours before clinical diagnosis, utilizing ICU patient records and comparing it with single models to enhance prediction accuracy. XGBoost, Light GBM, Random Forest Experimental but provides foundational knowledge. [36] Machine learning for early detection of sepsis: an internal and temporal validation study evaluates how deep learning models detect sepsis earlier and more accurately than other methods, using clinical data. Deep learning model: Multi-output Gaussian Process and Recurrent Neural Network (MGP-RNN). Comprehensive analysis with multi-center data [37] A Machine-Learning Sepsis Prediction Model for Patients Undergoing Hematopoietic Cell Transplantation develop an ML-based sepsis prediction model for hematopoietic cell transplantation (HCT) patients, using EHR to identify high-risk patients and improve early detection and outcomes. Convolutional Neural Network and Random Forest Preliminary findings with potential for future research [38] A machine learning model for early prediction and detection of sepsis in intensive care unit patients propose a machine learning model for early sepsis detection in ICU patients, comparing the performance of various models to improve accuracy and reduce mortality ensemble of SVM, RF, NB, LR, and XGBoost. Outperforms an accuracy of 0.96, demonstrating early sepsis detection in ICU patients. [40] EE550 The Potential Cost and Cost-Effectiveness Impact of Using Machine Learning Sepsis Prediction Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden and the United Kingdom To anticipate both the budget savings and improvements for patients that a three-hour-ahead sepsis-prediction tool could offer in ICU units in Sweden and the United Kingdom. Supervised Machine Learning Classifier They suggest that putting this sepsis prediction strategy in place can help save lives and lower costs in the ICU. [41] Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review To evaluate the impact of using unstructured clinical text combined with machine learning (ML) and natural language processing (NLP) techniques on the prediction, early detection, and identification of sepsis. ML and NLP Combining unstructured clinical text with structured data improves the accuracy and timing of sepsis prediction compared to using structured data alone, as evidenced by better AUC (Area Under the ROC Curve) scores. [43] Early Prediction of Sepsis Based on Machine Learning Algorithm To predict early sepsis 6 hours in advance by applying machine learning algorithms with different data processing methods to improve prediction accuracy. XGBoost and LightGBM algorithms were used. Two processing methods were compared: Mean processing method and Feature generation method. Both XGBoost and LightGBM achieved excellent performance, with AUC ranging from 0.910 to 0.979. [52] Supervised machine learning for early predicting the sepsis patient: modified mean imputation and modified chi-square feature selection enhance sepsis detection accuracy by using mean imputation and chi-square feature selection techniques, achieving improved performance and reduced processing time. Logistic Regression, Random Forest, or Support Vector Machine (SVM). Used modified mean-imputation and Chi-square for feature selection, focused on accuracy and recall, precision, and f1 score metrics. [53] Interpretable machine learning for early prediction of prognosis in sepsis: a discovery and validation study develop and validate an interpretable ML model for predicting mortality in sepsis patients, using clinical features and SHAP for model interpretability. eXtreme Gradient Boosting (XGBoost) detection of sepsis using a multivariate time series of physiological measurements in ICU patients. Outperformed MGP-RNN. [54] Mchine learingfor the prediction of acute kidney injury in patients with sepsis To develop and validate machine learning models for early prediction of acute kidney injury (AKI) in critically ill patients with sepsis, aiming to improve timely intervention and patient prognosis. Several ML algorithms were applied: logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN) The XGBoost model achieved the highest predictive performance with an AUC of 0.821, outperforming all other ML models and clinical scores. [55] A review of feature selection methods for machine learning-based disease risk prediction To provide a comprehensive overview of feature selection methods in machine learning, focusing on improving disease risk prediction from genotype data by identifying relevant features (SNPs) and addressing the challenges caused by high-dimensional genetic datasets. various feature selection techniques Machine learning helps detect patterns in large, complex genotype datasets for precision medicine. Feature selection improves model generalizability by removing irrelevant, noisy, or redundant features and retaining only the most informative ones. [57] Feature Extraction and Selection in Hidden Layer of Deep Learning Based on Graph Compressive Sensing To address feature redundancy in the hidden layers of deep learning models when processing high-dimensional, multi-modal data by proposing a novel feature extraction and selection method. feature extraction and selection method based on graph compressive sensing High-dimensional, multi-modal data often contain redundant features in deep learning hidden layers. The proposed graph compressive sensing method effectively extracts low-dimensional features while eliminating redundancy. [59] On classifying sepsis heterogeneity in the ICU: insight using machine learning To improve sepsis prediction from electronic health records (EHR) by stratifying ICU patients into clinically significant sepsis subpopulations based on distinct organ dysfunction patterns, addressing the heterogeneity of sepsis. Random Forest, Gradient Boost Trees, Support Vector Machines Stratifying sepsis patients by organ dysfunction subtypes improved the models’ ability to distinguish septic from non-septic ICU patients. [60] Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction To investigate the effects of data drift on machine learning models predicting sepsis onset from electronic health records (EHR) and to provide insights on how to monitor and retrain models effectively in changing clinical environments. eXtreme Gradient Boosting (XGB), Recurrent Neural Network (RNN) Monitoring infrastructure for sepsis prediction may be less demanding compared to other applications with more frequent data drift. [61] A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis To develop and validate machine learning models that predict 31-day mortality in patients presenting to the emergency department (ED) with sepsis, and to compare the models’ performance against internal medicine physicians and established clinical risk scores. Combination of laboratory and clinical data The ML models predicted 31-day mortality with AUCs of 0.82 (lab data only) and 0.84 (lab + clinical data). [63] Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals To develop and validate a machine learning algorithm (MLA) that predicts severe sepsis onset up to 48 hours in advance using readily available electronic health record (EHR) data. Diverse datasets from multiple health centers and community hospitals, using vital signs and other EHR data. The MLA demonstrated high predictive accuracy with AUROC of 0.931 at sepsis onset and 0.827 at 48 hours before onset on the testing dataset. [64] Development and evaluation of a machine learning model for the early identification of patients at risk for sepsis To develop a new machine learning-based sepsis screening tool, called the Risk of Sepsis (RoS) score, and compare its performance against established screening tools such as SIRS, SOFA, qSOFA, MEWS, and NEWS in emergency department patients. Gradient Boosting The RoS score outperformed all benchmark tools across multiple time points (1, 3, 6, 12, and 24 hours) with AUROC between 0.93 and 0.97. [65] LiSep LSTM: a machine learning algorithm for early detection of septic shock To develop and evaluate “LiSep LSTM,” a Long Short-Term Memory neural network model for early identification of septic shock in ICU patients. LSTM neural network LiSep LSTM outperformed a less complex model using the same features and targets. Achieved an AUROC of 0.8306 (95% CI: 0.8236–0.8376). [66] Early detection of sepsis utilizing deep learning on electronic health record event sequences To develop a deep learning model for early sepsis detection using a diverse multicenter dataset that overcomes limitations of prior models—such as limited clinical parameters, ignoring clinical interventions, and focusing only on ICU data—thereby expanding applicability beyond intensive care units. Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) The system learns feature interactions directly from raw data without manual feature extraction, outperforming baseline models. [68] Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs To validate a gradient boosted ensemble machine learning algorithm for early sepsis detection and prediction using electronic health records, and compare its performance with existing clinical scoring systems. Gradient Boosted ensemble model The machine learning algorithm (MLA) achieved AUROC scores of 0.88 at sepsis onset, 0.84 at 24 hours prior, and 0.83 at 48 hours prior to sepsis onset. [71] Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician To review recent and potential future applications of artificial intelligence (AI), machine learning (ML), and deep learning in infection research and clinical practice. The review covers a broad range of AI/ML/deep learning applications rather than focusing on a specific model AI shows promise in multiple infection-related domains but most studies lack real-world clinical validation and utility metrics. [76] The signature-based model for early detection of sepsis from electronic health records in the intensive care unit To develop an automatic, signature-based regression model for predicting a patient’s risk of sepsis over time using physiological data streams in the ICU. Gradient boosting machine (GBM) The signature-based approach offers a competitive and systematic way to model sepsis risk using streaming health data. [77] Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning To develop and validate a machine learning system for early prediction of sepsis in ICU patients using a large, multinational, multi-center dataset. deep self-attention model trained on 156,309 ICU admissions from five harmonized ICU databases across three countries The model predicted sepsis with an average AUROC of 0.847 ± 0.050 in internal out-of-sample validation and 0.761 ± 0.052 in external validation. [78] Improving Patient Outcomes Through Effective Hospital Administration: A Comprehensive Review To review the critical role of effective hospital administration in improving patient outcomes by exploring key components, strategies, measurement methods, and future trends in healthcare management. Patient-centered care and interdisciplinary collaboration are vital for better outcomes. A collective effort by healthcare leaders and policymakers is needed to develop skilled administrators, invest in technology, promote value-based care, and reduce disparities. [79] Digital health data quality issues: systematic review To develop a consolidated Digital Health Data Quality (DQ) Dimension and Outcome (DQ-DO) framework that identifies the key dimensions of digital health data quality, their interrelationships, and their impacts. Analyzed 227 peer-reviewed articles focused on digital health data quality in hospital settings. The framework highlights the complexity of digital health data quality and aids healthcare executives in prioritizing DQ improvement efforts. [81] Ethical issues in biomedical research using electronic health records: a systematic review To systematically review and analyze the ethical issues related to the use of electronic health records (EHRs) in research, focusing on challenges in managing access and governance. Employed constant comparative method to identify common ethical themes and summarized empirical studies descriptively. The digital transformation of healthcare is reshaping concepts of privacy, beneficence, and ethics in healthcare, research, and public health. [82] A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice To develop and implement a machine learning algorithm for predicting severe sepsis and septic shock in non-ICU hospital admissions and evaluate its impact on clinical practice and patient outcomes. Random Forest Classifier The algorithm achieved 26% sensitivity, 98% specificity, 29% positive predictive value, and a positive likelihood ratio of 13. [58] Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU develop machine learning models to predict in-hospital death risk in ICU sepsis patients, aiding physicians in making timely clinical decisions. LASSO, Random Forest (RF), Gradient Boosting Machine (GBM), and Logistic Regression (LR). Evaluated ICU patients with sepsis, focused on overall performance and discrimination metrics like Brier score and AUROC. [62] A machine learning sepsis prediction algorithm for intended intensive care unit use (NAVOY Sepsis): proof-of-concept study develop the NAVOY Sepsis machine learning algorithm for early sepsis detection in ICU patients, using routinely collected data for clinical use in European ICUs. Convolutional neural networks Aimed at early sepsis prediction, high AUROC on training and test data, focused on sensitivity, specificity, and accuracy. [67] Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals develop ML algorithm for predicting sepsis up to 48 hours before onset using patient data, aiming to improve early detection and patient outcomes. logistic regression, naive Bayes, SVM, KNN, Gaussian process, random forest, AdaBoost, gradient boosting Focused on machine learning prediction models for infant sepsis using EHR data. [73] Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare develop the SERA algorithm, which uses both structured data and unstructured data to predict sepsis, improving early detection and reducing false positives. Lasso Regression, SHAP, ML methods (XG Boost, Random Forest, Naive Bayes, LR, SVM, KNN, Decision Tree Aimed at predicting in-hospital mortality, used SHAP for feature importance analysis. [75] Impact of a deep learning sepsis prediction model on quality of care and survival evaluate the COMPOSER deep-learning model on sepsis prediction, focusing on its effects on mortality, sepsis bundle, and organ failure. Deep Learning Model: COMPOSER COMPOSER model: 1.9% reduction in sepsis mortality and 5% increase in sepsis bundle compliance, 4% reduction in 72-hour SOFA score [84] Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers to develop and validate an interpretable ML model using clinical features and inflammatory biomarkers to predict mortality risk in critically ill sepsis patients. eXtreme Gradient Boosting (XGBoost) XGBoost model achieved an AUC of 0.94 and an F1 score of 0.937 [88] A gradient boosting machine learning model for predicting early mortality in the emergency department triage: devising a nine-point triage score evaluate a ML model for predicting mortality in the emergency department, to improve patient categorization and resource allocation. Gradient Boosting Focused on increasing sensitivity for sepsis prediction using mean imputation and chi-square feature selection. Achieved 97.67% classification accuracy and improved processing time. [94] Early detection of sepsis utilizing deep learning on electronic health record event sequences To build a sepsis early notice model that surpasses previous models by being applicable outside of ICUs, having access to more data and not needing manual feature engineering. A hybrid architecture combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) The predictive ability ranged from 0.856 (24 hours before) to 0.756 (1 hour before) AS onset. [95] Machine learning for early detection of sepsis: an internal and temporal validation study To check if a deep learning model spots sepsis more correctly than standard machine learning and scoring methods, with results that reflect real-world clinical experience. Random Forest (RF), Cox Regression (CR), Penalized Logistic Regression (PLR), Multi-output Gaussian Process combined with a Recurrent Neural Network (MGP–RNN) The model that uses both machine learning and recurrent neural networks (MGP–RNN) had a better C-statistic of 0.88 for predicting sepsis within 4 hours than RF (0.836), CR (0.849), PLR (0.822) and clinical scores SIRS (0.756), NEWS (0.619) and qSOFA (0.481). [98] Early prediction of sepsis in the ICU using machine learning: a systematic review To examine and evaluate scientific studies that apply machine learning to predict sepsis in adult intensive care unit (ICU) patients. Multiple ML algorithms reviewed Most of the methods (86.4%) evaluated using offline data and horizon evaluation; almost no studies considered online training scenarios. It points out that there are major issues in reviews, including a lack of similarity, trouble with reproducibility and many different approaches. Table 4. is a compilation of studies focused on machine learning models for sepsis prediction and early detection, especially in ICU and emergency departments. Each study indicates the objectives, machine learning models applied, and the most important findings in terms of accuracy, time of early prediction, and performance metrics of the model. For example, some research studies use models such as XGBoost, Random Forest, and Convolutional Neural Networks to improve the prediction abilities, while others focus on the interpretability of the model, feature selection techniques, and data handling methods for better results. The studies reveal the potential of machine learning in the early detection, mortality prediction, and better patient outcomes in sepsis care. 4. Key Factors Affecting Sepsis Prediction Model Performance Sepsis prediction models utilize machine learning to analyze clinical data and give early warnings for the development of sepsis. The following points summarize the critical factors that affect their performance and reliability: 4.1. Type of Data Used The type of data used within a sepsis prediction model determines its overall accuracy and reliability. Some studies integrate both structured and unstructured data, including electronic health records, laboratory results, and vital signs, as well as unstructured data, such as clinical notes or free-text data, which may offer more comprehensive views of a patient's condition [72]. Other models may look specifically at structured data, such as vital signs or lab results, meaning their scope of prediction may be slightly narrowed but can be built to work much faster and possibly even more efficiently. Different data types can also blend well together to improve the outcome of the model because, especially in sepsis, multiple factors are complex. 4.2. Generalization to Unseen Data In summary, sepsis prediction models need to avoid overfitting the training data. Overfitting occurs when a model performs well on the data it was trained on but fails to generalize to new, unseen data [69]. Poor real-world clinical performance can be caused when, outside of training datasets, actual patient data differs. Fundamentally, such a model is robust enough to generate effectively across different populations and diverse settings. Techniques like cross-validation and the use of independent test sets can facilitate proper generation by models, avoiding heavy dependence on specific datasets but instead adapting to diverse clinical environments. 4.3. Model Transparency ("Black Box" Nature) Quite several advanced machine learning models, such as deep learning, present a "black box problem" because of the complexity; these models predict correctly; however, in a real clinical setting, such decision-making is not clear, rendering it difficult to have the interpretation of the same. Algorithm transparency is essential to winning over the trust of the clinicians and ensuring the clarity of the model recommendations with clear actionability [70]. Techniques such as SHAP (SHapley Additive exPlanations), or LIME (Local Interpretable Model-agnostic Explanations) could help shed light on what kind of input features make such differences in the model output, which is even a life-or-death issue in healthcare. 4.4. Addressing Data Imbalance Sepsis is usually a rare occurrence, making datasets used for training predictive models imbalanced, having significantly fewer sepsis events than those of non-sepsis events. Data imbalance can then lead to skewed predictions, where the model may favor the majority class (non-sepsis) and miss out on actual cases of sepsis. Improvement can also be realized in the model's capacity to correctly classify sepsis cases by applying various data imbalance techniques, like oversampling the minority class of cases (sepsis cases) or under sampling the majority class of cases (non-sepsis cases) [73]. Other data generation techniques, such as SMOTE - Synthetic Minority Over-sampling Technique can generate synthetic data, providing an opportunity for better-balanced training that should result in more precise and accurate prediction models. 4.5. Sensitivity to Sepsis Events Considering that early detection is critical, models must be sensitive to sepsis events. In other words, they should not produce false negatives, or miss a patient who has sepsis. High sensitivity means that the models identify as many of the sepsis cases as possible so that appropriate interventions can be made on time and may save lives. However, sensitivity typically comes with a cost to specificity: correctly identifying that the individual does not have sepsis. And balance needs to be maintained across these measures. Performance metrics of AUC-ROC and precision-recall curves help judge how well a model is classifying cases as either sepsis or a non-sepsis event, maximizing sensitivity [74]. 4.6. Choice of Performance Metrics The right selection of performance metrics will help to evaluate the efficacy of the sepsis prediction model. Though useful, the standard metrics include accuracy, sensitivity, specificity, and precision. The metrics fail to cover the clinical significance of the model in detail. For example, the use of precision-recall curves is significant when the case of sepsis is infrequent, as it happens in the detection of sepsis. The area under the curve of precision-recall gives a more meaningful evaluation when dealing with imbalanced data sets [74]. How the performance metrics are in line with clinical goals of minimizing false negatives to help in early intervention and reduce delays in treatment is essential for improving the outcomes of patients. Therefore, based on these key factors, better sepsis predictive models can be developed to be more precise reliable, and clinically relevant enough to improve patient outcomes and reduce mortality from sepsis. 5. MODEL VALIDATION TECHNIQUES Model Validation Techniques: Machine learning models, especially in healthcare, require comprehensive development and validation to guarantee their reliability and accuracy. Validating a model not only assures that it performs well on the training data but also that it can generalize to unseen real-world data, where the predictions can have serious clinical implications. This process involves several critical steps: preparing high-quality data, choosing appropriate algorithms, performance evaluation, interpretability, and addressing ethical issues. All of these steps are important in developing robust models that could help clinicians make decisions. The following sections detail the most important model validation techniques, providing best practices on how to optimize performance and ensure successful deployment in healthcare settings such as sepsis prediction. 1. Data Preprocessing and Feature Engineering: Data quality is ensured by cleaning inconsistencies and normalizing features. Related clinical features relevant to the cases in focus should be brought forward or extracted from varied sources (Electronic Health Records, labs, and vital signs) to allow a very accurate prediction Addressed missing data can then come as second through replacement/selection processes involving Chi-square mean imputation. Redundant features need to be eliminated for better performance of the model, and robust features must be derived from high-dimensional data for better reliability in prediction. 2. Model Selection and Training: For the proper selection of algorithms suitable for machine learning, the task being processed and the characteristics of data to be handled are essential considerations. Several algorithms were reviewed in the document including Decision Trees, Support Vector Machines, Logistic Regression, Gradient Boosting Machines, Neural Networks, and Ensemble Methods. Special techniques ought to be implemented especially because sepsis onset events are often rare in nature. In addition, one must ensure the ability of the model to generalize for unseen data through cross-validation and overfitting through excessive complexity. 3. Model Evaluation and Validation: It is important to check the model using appropriate metrics, such as AUC, sensitivity, specificity, accuracy, and precision-recall curves. The model must be tested on various timelines and disease stages to test the reliability and generalization ability in dynamic clinical settings. The validation should be done on different datasets such as internal and external to validate the generalization capability of the model. Comprehensive testing on diverse patient datasets is required to verify improvements in early detection, prediction accuracy, and overall reliability. 4. Interpretability and Explainability: Models or techniques that have interpretability should be selected so that clinicians can understand the rationale behind predictions. For example, tools like SHAP (SHapley Additive exPlanations) can be used to explain how different features contribute to predictions, which adds to the transparency and support for clinical decision-making. 5. Addressing Ethical and Data Privacy: Ethical guidelines should be followed. These include data privacy, patient consent, and the responsible use of data. The development of the model should be such that it adheres to these principles, thereby protecting patient information and trust in the model's predictions. 6. Challenges and Limitations of Machine Learning Techniques for Sepsis Detection This paper's review presents several challenges and limitations that need careful consideration, especially in methodology, data interpretation, and scope of analysis. Challenges facing this paper include availability and quality of data, class imbalance, generalizability, interpretability, clinician trust, and ethical and legal issues. Ethical and legal considerations also arise. Each of these factors holds a crucial position in the reliability and applicability of machine learning models in healthcare, so a balanced approach is needed to overcome these limitations. Discussion of these challenges is as follows. 6.1 Data Availability and Quality The quality and completeness of the data are crucial to the development of reliable ML models. Clinical datasets, which often originate from EHRs, are often characterized by missing or incomplete entries, thereby limiting their utility for training and validating models. Noise and inconsistencies in data further complicate the process of building robust models. Standardizing data collection practices and addressing data gaps are critical to improving model performance [27, 28, 29 and 31]. 6.2 Class Imbalance and Rare Events Sepsis is a rare event compared to other conditions, leading to an imbalance in datasets where septic cases are significantly outnumbered by non-septic cases. This imbalance can bias ML models toward predicting the majority class, reducing their ability to detect true positives. Techniques like oversampling and synthetic data generation have been employed but may lead to overfitting. Balancing datasets remains an ongoing challenge [33, 35, 38, 52 and 62]. 6.3 Generalizability Across Clinical Settings Models often fail to generalize effectively when applied to new datasets or healthcare settings due to differences in population characteristics, clinical practices, and data collection protocols. This lack of generalizability limits their usability across diverse healthcare environments. External validation using independent datasets is essential to assess robustness but is hindered by privacy concerns and regulatory barriers [36, 37, 38 and 58]. 6.4 Interpretability and Clinician Trust Many ML models, particularly those based on deep learning, are perceived as "black boxes," making their predictions difficult for clinicians to understand. This lack of interpretability can erode trust and hinder adoption in clinical practice. Tools like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) help provide insights into feature importance but often fail to address the underlying complexity of the models. Bridging the gap between accuracy and interpretability is vital for clinical acceptance [32, 34, 53 and 67]. 6.5 Ethical and Legal Concerns The use of patient data in ML models raises ethical and legal challenges, particularly concerning data privacy, informed consent, and fairness. Regulations like GDPR impose strict compliance requirements for data handling and sharing, which can slow down model development. Additionally, biases in datasets may lead to unfair predictions, exacerbating healthcare disparities. Ensuring ethical integrity and compliance is a critical consideration in ML deployment [73, 75, 84 and 88]. 7. Discussion Early detection of sepsis has a significant impact on patient outcomes. Awareness of sepsis especially at the initial level is vital since it helps in early intercession leading to multiorgan dysfunction and consequently improved patients’ survival. Due to the complexity of the task, machine learning has been identified as an important tool in this field. Due to the ability to process large volumes of data quickly, sepsis detection systems based on ML give real or early warning signals. It enables the clinicians to intervene before the disease progresses, this is always beneficial in saving lives. These new detection systems have significant clinical applications ranging from shorter ICU stays and corresponding lowered hospital costs to an overall enhancement of the patient’s care. Indeed, the definition of sepsis has been changing over time, and any alteration in the definition of sepsis affects machine learning models. Heterogeneity in definitions results in differences in cohorts of patients, and this impacts the procedure in model development and testing. It is also critical to understand how these definitions impact the model performance and more importantly, the models remain stable even when the definitions change. It is crucial to comprehend the field of machine learning encompasses an array of models amenable to a vast variety of tasks but that have their peculiarities and shortcomings. Some algorithms like deep learning are good at working with large data and complex relationships but are data-hungry, are usually black boxes, and can lack scalability. On the same note, while complex models like artificial neural networks do provide accuracy, other models, for instance, logistic regression models, provide accuracy but with certain amounts of simplicity in their workings. Compiling a careful analysis of these models, it is possible to pinpoint which is the most suitable for sepsis detection considering the conditions that the model must address. Several issues are tied to the identification of sepsis using the technique of machine learning. Another challenge related to datasets is the number of sepsis patients can be tens or hundreds of times less than in the non-sepsis group. Correspondingly, such an imbalance of data distribution may lead to chiefly focusing on the events of the majority class and missing important sepsis cases. The other challenge is that medical data has a lot of noise which can be defined as any unwanted signal that can be present in data. Many forecasting models are highly sensitive to errors in the data, the presence of missing values, and other similar discrepancies that reduce the predictive accuracy of the model. Solving these challenges requires the use of complex pre-processing of the models and related methodologies. Besides the factual constraints, one identifies the following questions of ethical nature. There are issues of privacy, security, and consent whenever a patient’s data is used in machine learning. These ethical issues have to be met to ensure that machine learning has been deployed in responsibly detecting sepsis. 8. Conclusion Deep learning (DL) and machine learning (ML) models have the incredible potential to improve early sepsis recognition. This is crucial for improving the patient's survival rate and efficiently reducing the morbidity and mortality associated with life-threatening situations. By utilizing various data sources, like electronic health records (EHRS), vital signs, as well as laboratory results, the above models provide accurate and timely predictions that outclass conventional diagnostic techniques. This was to ensure that the final SLR included only studies of high quality and relevance, with 80 studies summit the essential norms for insertion in the final study. Despite their potential, several challenges hinder the deployment of these technologies in clinical settings. Data quality challenges, such as missing or inconsistent records, and the unbalancing between septic and non-septic patients present a significant barrier to model accuracy. Moreover, the complicated nature of deep learning algorithms, often interpreted as "black boxes," complicates physician trust and adoption. The integration of strong feature engineering, good data standardization, and more interpretable models are extremely important to overcome such obstacles and strengthen model consistency. Additionally, attempting to address the moral and legal consequences related to patient privacy protection, written consent, and equity is extremely critical. Ensuring compliance to regulate gdpr while reducing discrimination in predictive models is vital to fostering trust in these technology development solutions. Overall, ml and dl models present a groundbreaking potential for sepsis diagnosing, also balanced approach for addressing these challenges would be necessary for their wide adoption and efficiency in clinical experience. 9. Future Work Future research in; machine learning for sepsis identification must focus on dealing with the challenges of data integrity and class imbalance. As sepsis is a rare occurrence, strategies for addressing these problems, including advanced data augmentation methods and synthetic data processing, will be crucial to improve model performance. Additionally, the development of real-time prediction mechanisms that can constantly monitor patient records and trigger early interventions could significantly reduce the time to treatment, thus improving survival rates. Researchers should further explore using multi-modal data, including both structured and unstructured clinical information, for a more comprehensive understanding of patient conditions and improved prediction accuracy. Another important point for future work is to improve the interpretability of advanced ML models, especially deep learning models, to make them clearer and more understandable to health professionals. Using tools like SHAP and LIME, researchers can gain additional insight into the importance of features, but further development will provide more actionable insights for clinical decision-making. Additionally, as healthcare settings vary widely, external validation and adaptation of these models to diverse medical environments are essential for ensuring generalizability. Future studies should also explore integrating these technologies into clinical workflow, ensuring they are all effective and easily implemented by healthcare providers. The focus must be on ensuring that such models not only enhance sepsis detection but also contribute to improving overall patient outcomes and healthcare system performance. Abbreviations Intensive Care Units (ICUs) Machine Learning (ML) Deep Learning (DL) Electronic Health Records (EHRs) Systematic Literature Review (SLR) Multi-task Gaussian Process Recurrent Neural Network (MGP-RNN) Support Vector Machine (SVMs) Gradient Boosting Machines (GBM) Random Forest (RF) Convolutional Neural Networks (CNN) SHAP (Shapley Additive Explanations) Declarations Ethics Approval and Consent to Participate: Not applicable. This study is a systematic literature review and does not involve any human participants or personal data. Clinical trial number: Not applicable. Consent for Publication: Not applicable. Availability of Data and Materials: This is a review article and does not contain any primary data. All data analyzed during this study were obtained from previously published articles in publicly available databases such as IEEE Xplore, ACM Digital Library, and Scopus. A complete list of the references is included in the manuscript. Competing Interests: The authors declare that they have no competing interests. Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors’ Contributions: MZ: Conceptualization, methodology design, data curation, initial manuscript draft. ID: Supervision, critical revision, and correspondence handling. NS: Literature review, analysis of machine learning techniques. BE: Analysis of datasets and medical interpretation. SM: Draft refinement, formatting, and proofreading. ZT: Supervision, technical review, validation, and co-correspondence. All authors have read and approved the final manuscript. References Fleuren, L. M., Klausch, T. L., Zwager, C. L., Schoonmade, L. 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Tables Table 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6731418","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467079816,"identity":"451c0805-047e-43ac-9204-d23272c540f1","order_by":0,"name":"Muhammad Zubair","email":"","orcid":"","institution":"Superior University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Zubair","suffix":""},{"id":467079817,"identity":"540dc19d-a15f-4b2a-98bd-1b1aff556d35","order_by":1,"name":"Irfanud Din","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoElEQVRIiWNgGAWjYBACNgbGBwwMFaRpYTZgYDhDmj1ALYxtpGjgkz7M+Llw3h05gxs5Bsw8f4hxGF8ys/TMbc+MIVp4iNHCw39Amnfb4cRtYC0SRGlhZv7NO+dwPUSLAXFa2KR5Gw4nmIG1JBCpxZrn2GHD/WeeFRycc4AILfI9zMy3eWoOy0u2J2988IaYEEMBxNgxCkbBKBgFo4AYAACVsCxZ7z808wAAAABJRU5ErkJggg==","orcid":"","institution":"New Uzbekistan University","correspondingAuthor":true,"prefix":"","firstName":"Irfanud","middleName":"","lastName":"Din","suffix":""},{"id":467079818,"identity":"7658321c-f568-4743-b57b-ee068d305fb1","order_by":2,"name":"Nadeem Sarwar","email":"","orcid":"","institution":"Bahria University","correspondingAuthor":false,"prefix":"","firstName":"Nadeem","middleName":"","lastName":"Sarwar","suffix":""},{"id":467079819,"identity":"355ce05a-1ea8-4d26-b100-2f5ace13e2a2","order_by":3,"name":"Botir Elov","email":"","orcid":"","institution":"Alisher Navo'i Tashkent State University of for Uzbek Language and Literature","correspondingAuthor":false,"prefix":"","firstName":"Botir","middleName":"","lastName":"Elov","suffix":""},{"id":467079820,"identity":"6f63e9ba-1603-47ff-8ad9-77066732f8fb","order_by":4,"name":"Samariddin Makhmudov","email":"","orcid":"","institution":"Mamun University","correspondingAuthor":false,"prefix":"","firstName":"Samariddin","middleName":"","lastName":"Makhmudov","suffix":""},{"id":467079821,"identity":"68ebb90f-6386-4b38-93cc-29085ad54a6a","order_by":5,"name":"Zouheir Trabelsi6","email":"","orcid":"","institution":"United Arab Emirates University","correspondingAuthor":false,"prefix":"","firstName":"Zouheir","middleName":"","lastName":"Trabelsi6","suffix":""}],"badges":[],"createdAt":"2025-05-23 09:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6731418/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6731418/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-11423-2","type":"published","date":"2025-10-23T16:16:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84340302,"identity":"008bbf63-4632-41c0-abe9-e0809f46a64d","added_by":"auto","created_at":"2025-06-10 18:24:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73352,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Priorities in Sepsis from a Stakeholder Perspective\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6731418/v1/e5da141cb2aa46b71d07b2cb.png"},{"id":84339548,"identity":"9ba2d2d2-38ec-44ce-8c96-80fcee0d3083","added_by":"auto","created_at":"2025-06-10 18:16:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68170,"visible":true,"origin":"","legend":"\u003cp\u003eSearch Results of Database\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6731418/v1/a59e0e2f0f452911ea9792e5.png"},{"id":84339557,"identity":"a8e2b7d5-48ae-4193-b86e-a1a3a0ed1a9c","added_by":"auto","created_at":"2025-06-10 18:16:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93045,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Selection\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6731418/v1/1f6da6e26167bb05fb9e0235.png"},{"id":84339556,"identity":"e23b8cc0-f9b1-439b-a213-2c2ea47e6c4d","added_by":"auto","created_at":"2025-06-10 18:16:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71531,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Selection Criteria\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6731418/v1/21f4b6e80bdf8e4895fe316a.png"},{"id":84339549,"identity":"eb5a1203-78f3-4c7c-81d6-e11a7c8732d8","added_by":"auto","created_at":"2025-06-10 18:16:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":72670,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of Articles Selected Across Year\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6731418/v1/233f4f00e6d974390e576c8c.png"},{"id":84339551,"identity":"6a866e2f-4a07-4c89-b994-031452b2aa8e","added_by":"auto","created_at":"2025-06-10 18:16:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":94774,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental Design Choices for Sepsis Onset Prediction\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6731418/v1/d4465274ea32c2de1846226e.png"},{"id":84339554,"identity":"f43208b1-b65f-4cce-b8ea-0649a7dc94ed","added_by":"auto","created_at":"2025-06-10 18:16:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":56335,"visible":true,"origin":"","legend":"\u003cp\u003eFeature Importance\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6731418/v1/f6cf432dbd6a9965141ef003.png"},{"id":94490472,"identity":"bd52eb10-6201-4c09-85fc-384d057f4065","added_by":"auto","created_at":"2025-10-27 17:10:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1703948,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6731418/v1/4a892618-f1f0-419a-b190-f1bd7f61427f.pdf"},{"id":84339555,"identity":"d9802cf4-8e28-422a-887b-9546e5238da3","added_by":"auto","created_at":"2025-06-10 18:16:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19841,"visible":true,"origin":"","legend":"","description":"","filename":"Table1and2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6731418/v1/e4913b5003439ff61916014d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Revolutionizing Sepsis Diagnosis Using Machine Learning and Deep Learning Models: A Systematic Literature Review ","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSepsis is a potentially lethal situation induced by the body\u0026apos;s reaction to infection and continues to be one of the great global challenges, most dramatically seen in developing nations [1]. Sepsis is among the leading causes of death in intensive care units (ICUs) worldwide and its recognition, particularly in the early stages of the disease, remains a medical challenge. The advent of an affluence of available digital health data has created a setting in which machine learning can be used for digital biomarker discovery, with the ultimate goal to advance the early recognition of sepsis [2]. The synergism between constrained healthcare resources, disparities in the economy, and weak infrastructure increases the incidence of sepsis [3].\u003c/p\u003e\n\u003cp\u003eIn developing countries, the main barriers to timely detection and effective management of sepsis include limited access to healthcare services, lack of essential medical supplies, and insufficient healthcare infrastructure. Socioeconomic factors such as poverty and malnutrition also increase susceptibility to infections, thus amplifying the risk of developing sepsis. Addressing these challenges is important not only in reducing illness and mortality due to sepsis [4, 5]. Sepsis is still a leading cause of mortality in the critically ill patient, especially in the intensive care unit and emergency department. Early detection and timely intervention improve the outcome of the patient. The application of ML models is becoming a promising tool for enhancing the prediction and diagnosis of sepsis through clinical data and advanced algorithms for identifying high-risk patients. The research collection discusses diverse ML techniques: from traditional models, such as Random Forest and Logistic Regression, to more complex approaches, like Convolutional Neural Networks and ensemble methods. It demonstrates potential for improving detection of sepsis, prediction of mortality, and clinical decision-making.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e1.1 Importance of early diagnosis\u003c/h2\u003e\n\u003cp\u003eEarly diagnosis of sepsis is important because the condition progresses very rapidly in a medical emergency. If untreated, it leads to failure of the organs and subsequently death. It is brought about by an inappropriate immune response to an infection, with the associated massive inflammation and tissue destruction, potentially escalating into septic shock-a life-threatening condition, with severely low blood pressure and multi-organ failure [6]. Timely detection enables healthcare providers to administer life-saving interventions such as antibiotics, fluid resuscitation, and supportive therapies, significantly improving patient outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe research interest among different stakeholders in sepsis reveals the collaborative approach to fighting this life-threatening condition. Institutions account for the highest percentage at 36%, with a focus on furthering theoretical understanding and finding new solutions. Clinical trials amount to 21% in translating research into practice, testing treatments and interventions. Pharmaceutical companies contribute 15% in developing therapies for sepsis-related complications. Laboratory studies, at 13%, deliver critical perceptions into the biological mechanisms of the disease, running embattled research. Patient advocacy groups, making up 9%, raise awareness, influence research priorities, and ensure patient-centered approaches, while policymakers, comprising 7%, shape healthcare policies, funding, and regulatory frameworks to support sepsis management. This distribution in Figure 1 below reflects a multidimensional effort to combat sepsis, aligning with the challenges and opportunities presented earlier. The involvement of diverse stakeholders, combined with advancements such as machine learning and digital biomarkers, is crucial for addressing the complex nature of sepsis and improving patient outcomes.\u003c/p\u003e\n\u003ch2\u003e1.2 Challenges in Sepsis Diagnosis\u003c/h2\u003e\n\u003cp\u003eStudies indicate that each hour of delay in treatment increases mortality risk by 7-8%, highlighting the time-sensitive nature of sepsis. Unlike many other diseases, its non-specific symptoms, such as fever and rapid heart rate, make early recognition challenging, yet essential, to prevent catastrophic outcomes and save lives [7, 8]. One major challenge with sepsis is that its underlying processes are not comparable to conditions like inflammation. These conditions share indicators such as altered blood pressure and heart rate, symptoms like fever, and consistent molecular patterns, including immune system imbalance. Due to the unique systemic characteristics of sepsis, the use of biological and molecular indicators (commonly referred to as biomarkers) has been proposed as a potential approach to improve the accuracy of sepsis diagnosis and identification [9, 10]. Although significant efforts have been made to identify suitable indicators, the low sensitivity and specificity have limited the broad acceptance of any single or combined biomarker for diagnosing and treating sepsis [11, 12]. This is despite the many efforts that have been undertaken.\u003c/p\u003e\n\u003cp\u003eThe development of data-driven biomarkers has seen significant progress in recent decades and holds promise for addressing current difficulties. The method aims to analyze and use health-related information (such as patient records or medical data) by using machine learning techniques that identify patterns and make predictions or decisions based on data. The availability of digital data at high resolution is progressively expanding, catering to those at risk of sepsis and those already suffering from sepsis [13]. The dataset encompasses a comprehensive range of information, including laboratory results, vital signs, genetic data, molecular data, clinical records, and health history. A comprehensive study of digital health markers enabled by flexible data use could replace the traditional narrow focus on evaluating only blood test indicators for health insights.\u003c/p\u003e\n\u003cp\u003eMachine learning algorithms offer a natural ability to effectively analyze complex and diverse digital patient data to accurately predict the development of sepsis in patients [14, 15]. This is achieved by identifying and using predictive patterns in the data. This allows us to accurately predict who is likely to develop sepsis. The identification of predictive patterns is usually performed using one of two approaches: Supervised Machine Learning or Unsupervised. Supervised learning algorithms are typically used to predict unlabeled data using labeled training data (e.g., whether a patient has sepsis or not) as a source of knowledge. In contrast, unsupervised learning does not involve the process of assigning labels to data. These algorithms are responsible for searching for patterns in the data, both familiar and unfamiliar. Many studies published in the past two decades have used various computer models to effectively predict the development of sepsis at the early detection stage [16, 18]. A proposed detailed framework was introduced called the multi-task Gaussian process recurrent neural network (MGP-RNN), which could be used for end-to-end training and handling of multiple tasks.\u003c/p\u003e\n\u003cp\u003eSeveral alternative approaches based on a combination of multi-task Gaussian processes and recurrent neural networks for the proposed system have been discussed. These are methods that indicate very accurate predictions for the onset of sepsis. As such, for instance, antibiotics were prescribed 17 hours late, and sepsis only 36 hours later [19]. In their paper [20], the authors further proposed that single-task Gaussian processes can be integrated into neural networks. They also introduced the concept of a \u0026quot;Gaussian process adapter,\u0026quot; which can be effectively applied within the end-to-end learning paradigm.\u003c/p\u003e\n\u003cp\u003eIn an earlier study, the substitution of the last fully connected layer with a Gaussian process adapter architecture that includes the usage of dynamic time warping along with a TCN termed MGP-TCN leads to a superior prediction of early detection of sepsis with much higher accuracy [21]. Considering such massive research in the direction along with its huge day-to-day improvement, it\u0026apos;s significant to time and again realize the new state-of-the-art improvements done in this area as well [22].\u003c/p\u003e\n\u003cp\u003eThis review shall outline the current state-of-the-art machine learning models for identifying potential digital biomarkers in the early detection of sepsis in an ICU. The study aims at filling in the gaps between conventional diagnostic methods and discovering innovative ways to improve outcomes. The key contributions to this review are listed as follows:\u003c/p\u003e\n\u003col class=\"decimal_type\"\u003e\n \u003cli\u003eSystematic Literature Review and Meta-Analysis: A demanding systematic review with meta-analysis is considered for the identification, critical appraisal, and synthesis of all the available evidence surrounding the early detection of sepsis through machine learning model applications. This involved appropriate, extensive searching of relevant databases using strict inclusion and exclusion criteria and critically appraising the included studies.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eComprehensive Analysis of Machine Learning Techniques: This review presents an in-depth analysis of various techniques of machine learning that have been applied in the field of sepsis detection, including traditional algorithms (decision trees, support vector machines) and advanced techniques, such as deep learning, and ensemble methods.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEvaluation of Model Performance: It examines the performance of a machine learning model in terms of sensitivity, specificity, and accuracy. Other relevant metrics to discuss are those that make model performance. These would include data quality, feature engineering, and model selection, among others.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eIdentification of Key Challenges and Limitations: The review identifies important limitations and challenges related to using machine learning for sepsis detection, such as low-quality data, low model interpretability, and limited ethical considerations.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eExploration of Future Research Directions: The review outlines promising avenues for future research that shall help address the challenges identified for the improvement of accuracy and reliability in machine-learning-based models for sepsis detection. These directions incorporate explainable AI models, multi-modal data integration, and advanced techniques for feature engineering.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eClinical Implications and Recommendations: It gives potential clinical implications on machine learning for early sepsis detection and proposes future recommendations for research and practice in the clinical sector. By providing a clear overview of the current landscape and future possibilities, this review aims to advance early sepsis detection and improve patient outcomes.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e1.3 Research Questions\u003c/h2\u003e\n\u003cp\u003eThe following research questions, which are derived from the stated objectives, need to be addressed to thoroughly investigate and achieve the goals outlined in the study, providing a clear direction for the research process.\u003c/p\u003e\n\u003cp\u003eRQ1: Which machine and deep learning algorithms have been used for sepsis prediction?\u003c/p\u003e\n\u003cp\u003eRQ2: How do the algorithms differ concerning predictive accuracy, data requirements, and clinical application?\u003c/p\u003e\n\u003cp\u003eRQ3: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; How do various machine and deep learning algorithms compare in predicting sepsis regarding accuracy, methodology, and performance metrics across different datasets?\u003c/p\u003e\n\u003cp\u003eRQ3: What strategies have been adopted in the existing research to navigate the ethical and data privacy challenges posed by applying machine and deep learning to sepsis prediction using healthcare data?\u003c/p\u003e"},{"header":"2.\tSystematic Review Procedure","content":"\u003ch2\u003e2.1 Search Strategy\u003c/h2\u003e\n\u003cp\u003eA multi-pronged approach was employed to ensure a comprehensive exploration of the topic. Initially, a thorough search was conducted across several esteemed databases, including IEEE Xplore, ACM Digital Library, and Scopus. These databases are renowned for their vast collection of articles spanning biomedical, technical, and interdisciplinary research domains. Table 1 below presents the search results obtained from these databases, highlighting the number of articles retrieved from each: 310 from IEEE, 206 from ACM, and 1,011 from Scopus. The search results from 3 different databases are as follows in figure 2 below,\u003c/p\u003e\n\u003cp\u003eThe efficiency of the literature search was also increased by identifying the Boolean operators to be used. These operators, especially \u0026ldquo;AND\u0026rdquo; and \u0026ldquo;OR,\u0026rdquo; made it easier to combine relevant search terms while isolating irrelevant articles [23, 24]. For instance, the following sources were generated: \u0026ldquo;Sepsis\u0026rdquo; joined with \u0026ldquo;Machine Learning\u0026rdquo; using the \u0026ldquo;AND\u0026rdquo; operator, avoiding articles that merely discuss the utilization of various machine learning approaches in the identification and diagnosis of sepsis. Furthermore, filters were applied to increase the degree of specificity in line with certain integrated parameters such as the kind of articles, date of publication, and language of the articles [25].\u003c/p\u003e\n\u003cp\u003eDue to the need to ensure the articles included in this review are as up-to-date as possible, a clear timeline by which the literature must have been published was set. The review has restricted its focus to articles published in the last two decades, outlining recent developments and trends in the field. This proposed strategy corresponds to methodologies used in literature reviews, where delimiting a specific time range has proven effective in maintaining the review\u0026rsquo;s relevance.\u003c/p\u003e\n\u003cp\u003eThus, the search strategy that has been put in place for this review has been designed to be comprehensive while also being thorough. By using the mean of highly recognized databases, application of the Boolean operators, and search filters as well as setting a clear temporal constraint for the inclusion, the aim of setting up the literature Scopus in Figure 3 was to capture the vast and diversified published literature in the field of predicting sepsis through machine and deep learning techniques.\u003c/p\u003e\n\u003ch2\u003e2.2 Study Selection Criteria\u003c/h2\u003e\n\u003cp\u003eThe process of selecting studies for a systematic literature review requires careful attention to ensure both relevance and completeness. This review relied on clearly defined inclusion and exclusion criteria, which played a key role in choosing the articles. For the inclusion criteria, preference was given to significant research studies that explored sepsis prediction using structured and thorough methods. These studies primarily focused on human participants to provide a better understanding of the real-world effectiveness of these algorithms. For example, the paper by [28] focuses on the application of a machine-learning framework for in-hospital mortality prediction for ICU patients with sepsis, demonstrating the potential and flexibility of machine learning in this domain.\u003c/p\u003e\n\u003cp\u003eBut still, it was important for the review to be fair and relevant. Thus, for these reasons, several sources were excluded. For language considerations, articles in non-English languages were omitted; they may be challenging to interpret and, in turn, affect its accuracy. Further, studies that did not involve machine learning or deep learning directly in the context of sepsis prediction, or studies that were not focused on human subjects, were excluded. For instance, research using a machine learning-based radiomics model in predicting HPV types in cervical cancer [31] provided relevant information but it was excluded since it does not focus on the interest of the review.\u003c/p\u003e\n\u003cp\u003e2.2.1 Screening Process\u003c/p\u003e\n\u003cp\u003eThe screening process was structured in a step-by-step manner. Titles and abstracts were screened first to filter out irrelevant studies. Then, full-text articles were critically assessed for eligibility based on the criteria. To ensure that the study selection process is reliable and consistent, an inter-rater reliability approach was adopted [26]. Inter-rater reliability corresponds to the agreement at which multiple reviewers independently review, and classify the same data, or studies. Two separate independent reviewers independently evaluated two independent reviewers for study inclusion eligibility using predefined criteria in the current review. To reduce bias and accuracy, an agreement between reviewers regarding exclusion/inclusion was sought by taking recourse towards a third reviewer for final resolving the discrepancies. It will minimize the chances of lowering reliability during the selection of the study. This approach ensures that the selection is both thorough and well-organized, combining structure with teamwork to maintain rigor and completeness [27, 28].\u003c/p\u003e\n\u003cp\u003eThe study selection criteria, planned and guided by a structured methodology, ensured the inclusion of high-quality. The study is relevant to research on sepsis prediction using machine and deep learning techniques. The criteria were designed to capture well-conducted studies on the clinical application of those algorithms. Each stage, beginning with defining criteria to the final assessments, was carried out maintaining accuracy, consistency, and relevance.\u003c/p\u003e\n\u003cp\u003eThis was done in two steps: the titles and abstracts were reviewed first to exclude irrelevant studies, followed by a detailed full-text assessment to confirm eligibility. This ensured that only relevant and high-quality studies were included.\u003c/p\u003e\n\u003cp\u003e2.2.2. Inclusion Criteria\u003c/p\u003e\n\u003cp\u003eIC1: Studies using clinical trials and datasets for the validation of the predictive models in sepsis outcomes.\u003c/p\u003e\n\u003cp\u003eIC2: Studies that have applied machine and deep learning techniques for sepsis prediction with human subjects.\u003c/p\u003e\n\u003cp\u003eIC3: Studies that are well-structured and have clinical applicability\u003c/p\u003e\n\u003cp\u003eIC4: Studies that were published between 2016 and 2025.\u003c/p\u003e\n\u003cp\u003e2.2.3 Exclusion Criteria\u003c/p\u003e\n\u003cp\u003eEC1: Studies that do not use structured methodologies and are not based on clinical trials or observational data related to sepsis prediction.\u003c/p\u003e\n\u003cp\u003eEC2: Studies that are not conducted to practice or do not demonstrate in practice the effectiveness of sepsis prediction models.\u003c/p\u003e\n\u003cp\u003eEC3: Research that was not specifically conducted on the prediction of sepsis by the machine or deep learning.\u003c/p\u003e\n\u003cp\u003eEC4: Researchers without human subjects and were not related to sepsis prediction.\u003c/p\u003e\n\u003cp\u003e2.2.4 Selection Process\u003c/p\u003e\n\u003cp\u003eThe selection process for the SLR started with obtaining articles from three important databases: IEEE, ACM, and Scopus. In total, 310 articles were retrieved using IEEE, 206 using ACM, and 1011 using Scopus. Duplicates are identified and extracted from the dataset, hence removing 600 entries. Articles in Chinese (75) and French (70) have been excluded due to restrictions on a language. After removing duplicates and language-specific articles, there were 782 articles remaining for screening. Articles from the screening stage that were out of date or outdated in their technique were excluded at this stage. 445 articles were removed from the pool of selections at this step. Then, 237 articles were requested to retrieve full texts, and a request was made to retrieve all those articles to examine further. However, 162 could not be retrieved, and the remaining 125 articles were forwarded for eligibility. The remaining articles were assessed for eligibility at the eligibility assessment stage. Of the 125 articles that were evaluated, 45 were excluded. The reasons for exclusion include review article 27, AURAC absent in studies 8, meta-analysis 5, conference paper 3, and book chapter 2. Following these exclusions, a total of 80 studies remained to be included in the final SLR. This detailed process ensured that only relevant and of high quality, meeting the necessary criteria studies were included in the review.\u003c/p\u003e\n\u003cp\u003eThe process of selecting relevant research papers for the study is illustrated in Figure 4, which presents a visual representation of the overall steps involved in the selection process. It outlines the key stages for identifying and choosing the most pertinent papers for the study.\u003c/p\u003e\n\u003ch2\u003e2.3 Distribution of Studies Across Years\u003c/h2\u003e\n\u003cp\u003eOf the reviewed studies, the temporal analysis indicates that interest in this research area remains constant from the observation period of 2016 to 2025. This distribution also reflects the concern for artificial intelligence in the prognosis of sepsis and the increase in publications on this aspect because of acknowledging sepsis as a major global health concern [93]. The number of selected papers over the years is illustrated in Figure 5 reflects that the count of selected papers was a little higher in the year 2020.\u003c/p\u003e\n\u003ch2\u003e2.4 Quality Assessment\u003c/h2\u003e\n\u003cp\u003eThe following criteria were selected to perform the quality assessment and relevance of the selected articles.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eData Preprocessed: Assess whether the dataset underwent cleaning, transformation, and normalization to ensure quality and usability.\u003c/li\u003e\n \u003cli\u003eData Source and Collection: Examines the origin and methodology used for gathering the data, ensuring reliability and relevance.\u003c/li\u003e\n \u003cli\u003eSource of Feature: Evaluate whether the features (variables) used in the analysis are well-defined and appropriate for the study objectives.\u003c/li\u003e\n \u003cli\u003eEthical Issue: Investigates adherence to ethical guidelines, such as data privacy, consent, and responsible usage.\u003c/li\u003e\n \u003cli\u003eDetail Discussion: Check if the study includes comprehensive explanations and critical analysis of the findings.\u003c/li\u003e\n \u003cli\u003eMeasurement of Model\u0026rsquo;s Performance: Reviews the metrics used (e.g., accuracy, sensitivity) to assess the effectiveness of the proposed model.\u003c/li\u003e\n \u003cli\u003eEvaluation Method: Analyze the robustness of validation techniques like cross-validation or independent testing for model performance.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis assessment focused on several key aspects, including data quality, preprocessing techniques, feature engineering, ethical considerations, model evaluation, and the level of detail in the discussion. Each study was evaluated against these criteria, and the results are summarized in the table below. A score of \u0026quot;1\u0026quot; indicates that the criterion was met, while \u0026quot;0\u0026quot; indicates that it was not. By systematically evaluating these factors, we aimed to identify studies that adhered to sound methodological practices and provided robust evidence to support their findings. Table 1 highlights the quality of the included studies. \u0026ldquo;0\u0026rdquo; is marked as not available/not present and \u0026ldquo;1\u0026rdquo; is marked as available or present.\u003c/p\u003e\n\u003ch2\u003e2.5 Experimental Design Choices for Sepsis Onset Prediction\u003c/h2\u003e\n\u003cp\u003eTo ensure the robustness, applicability, and relevance of the findings in real-world sepsis prediction, it is essential to evaluate the study\u0026apos;s methodological approach, data diversity, and the clinical utility of machine learning models. Following are the types of designs and techniques used as experimental designs for sepsis prediction are mentioned in Figure 6.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eRetrospective vs. prospective: Most studies used retrospective approaches to analyze past patient data. However, they provide rich datasets but cannot capture the ever-changing nature of clinical practice. Future designs that collect data in real-time will provide insights into how the models perform in real-world settings.\u003c/li\u003e\n \u003cli\u003eSingle-center vs. multicenter: Single-center approach extracts data from a single center, which limits the generalizability of the results. In contrast, multicenter studies improve the robustness of the model by including different datasets.\u003c/li\u003e\n \u003cli\u003ePrediction time window: Refers to how far in advance the machine learning model predicts sepsis relative to its diagnosis. This approach influences both the model\u0026apos;s accuracy and its practical utility in clinical settings, highlighting the importance of early detection for timely intervention.\u003c/li\u003e\n \u003cli\u003eVarious experimental design choices are employed in sepsis onset prediction in Table 2, which outlines key factors that influence the development and effectiveness of predictive models.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e2.6 Data Sources for Sepsis Detection\u003c/h2\u003e\n\u003cp\u003eThe features and data sources employed in sepsis detection using machine learning and deep learning models are diverse and extensive, reflecting the multilayered nature of the condition. The careful curation of relevant data points and reliance on comprehensive, accurate, and real-time data sources is imperative in the development of models that are not only high-performing but also clinically relevant and conducive to integration into existing healthcare systems [99]. The primary sources of data for these studies largely include.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eElectronic Health Records (EHRs): EHRs are a digital version of a patient\u0026rsquo;s paper chart and are a rich source of patient history, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, and laboratory test results. Many models leverage these data repositories due to the comprehensive nature of the patient data they contain.\u003c/li\u003e\n \u003cli\u003eVital Signs Monitors: Real-time data from monitors capturing patients\u0026apos; vital signs are particularly valuable for models focusing on early detection of sepsis, where timely intervention is critical.\u003c/li\u003e\n \u003cli\u003eDatabases and Health Registries: Some studies also utilize large-scale health databases and registries that collect data from various healthcare settings for a more population-based analysis.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe extracted data was organized to facilitate analysis and comparison, with Table 2 summarizing key data points. These include study design, which refers to the type of research structure such as prospective, retrospective, or experimental. Data sources are the origin of the data such as electronic health records, ICU databases, or simulated data. Sample size indicates the number of participants or data points analyzed. The quality tier assesses the study\u0026rsquo;s methodological rigor and reliability. These elements bring out trends and challenges in the field. Diverse datasets and rigorous analysis are underlined to boost prediction accuracy and guide further research directions.\u003c/p\u003e"},{"header":"3.\tOverview of ML, DL Models, and Their Features for Sepsis Prediction ","content":"\u003cp\u003eThe role of ML and DL is central to predictive technologies in healthcare, especially with sepsis detection. There has been an impressive array of computational methods from decision trees to neural networks, which have been effectively applied to develop early warning systems for sepsis. These models rely on several critical features, including physiological variables, laboratory results, and demographic data, that are essential for accurate prediction. This shows the potential of a novel, more effective approach toward sepsis prognosis and underlines the role that data-driven models play in improving patient outcomes by integrating diverse features with ML and DL models [91, 92].\u003c/p\u003e\n\u003cp\u003e3.1 Types of Models Used\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe section describes different variants of ML/DL models that have been proposed to determine and predict cases of sepsis based on their applications and are crucial for resolving the complexities regarding the processing of clinical data. There are quite popular techniques, namely Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, Gradient Boosting Machines, Neural Network methods, and ensemble or DL variants. Each of these methods contributes uniquely toward overcoming challenges in analyzing complex clinical data for early and accurate sepsis diagnosis, thus demonstrating the range of computational approaches that are under consideration in this field.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.1.1 Decision Trees and Random Forests\u003c/p\u003e\n\u003cp\u003eThe simple yet powerful model is referred to as a decision trees that function similarly to flowcharts: At each node, the features split the data, so the prediction happens. Particularly effective at dealing with data of both categories and types, they can be highly used in the analysis of clinical datasets for sepsis detection. However, they may overfit small datasets, thus being less generalizable. This limitation is addressed by Random Forests, which is an ensemble of Decision Trees, by aggregating predictions from multiple trees, thus reducing the overfitting and improving accuracy. This method is particularly useful in capturing nonlinear relationships in complex sepsis indicators and is widely applied in the analysis of physiological and clinical data.\u003c/p\u003e\n\u003cp\u003e3.1.2 Support Vector Machines (SVMs)\u003c/p\u003e\n\u003cp\u003eSupport Vector Machines are robust supervised learning models and can be very good with high-dimensional spaces. It finds the best hyperplane that separates data points into different classes. In nonlinear cases, the kernel function such as the radial basis function is used to project the data to higher dimensions. SVMs are especially useful in datasets having large numbers of variables. These include physiological and biochemical markers used for the detection of sepsis. SVMs are computationally expensive, and in some cases, they become computationally expensive for handling large amounts of data and hyperparameters which require careful tuning.\u003c/p\u003e\n\u003cp\u003e3.1.3 Logistic Regression\u003c/p\u003e\n\u003cp\u003eLogistic Regression is a statistical model for binary classification, used in tasks such as predicting the presence or absence of sepsis. It works on estimating probabilities through a logistic function and is valued for its simplicity and interpretability. The model is excellent when the relationships in the data are linear and, hence, it is a popular choice for the initial prognosis of sepsis. However, in reality, it fails to identify complex interactions between the variables, which limits its application in more complicated clinical datasets. Still, it acts as a reliable baseline model for sepsis prediction studies.\u003c/p\u003e\n\u003cp\u003e3.1.4 Gradient Boosting Machines (GBM)\u003c/p\u003e\n\u003cp\u003eGradient Boosting Machines are advanced ensemble methods where the decision trees are learned sequentially. The new tree learns the mistakes of the previous tree, and this process is continued for several iterations. Popular variants include XGBoost, LightGBM, and CatBoost. They can handle missing data, thus making them highly accurate for predictions. This type of model works very well for datasets involving clinical data with several features such as laboratory results and vital signs. While Gradient Boosting Machines are very accurate, they require careful regularization to avoid overfitting and are computationally very intensive for very large datasets.\u003c/p\u003e\n\u003cp\u003e3.1.5 Neural Networks\u003c/p\u003e\n\u003cp\u003eNeural Networks, a brain-inspired structure, are well suited for complex, high-dimensional datasets. CNNs are used widely to capture spatial patterns in data, such as time-series vital signs, while RNNs are well suited for handling sequential data and thus are better suited for tracking the progression of sepsis. Such models rely on layers of interconnected neurons to learn the intricate relationships within the data. However, they are often computationally intensive, need larger datasets for training, and are criticized for a lack of interpretability, which sometimes will not support clinical application.\u003c/p\u003e\n\u003cp\u003e3.1.6 Ensemble Methods\u003c/p\u003e\n\u003cp\u003eEnsemble methods combine the strengths of multiple machine learning models to improve overall prediction accuracy and stability. Some techniques applied in sepsis detection include bagging, boosting, and stacking. Models like Random Forest, Gradient Boosting, and Logistic Regression can be combined for better robustness and bias or variance reduction. Ensemble methods are especially useful in clinical environments where reliability is essential. These methods are computationally expensive and less interpretable than a single model despite their accuracy.\u003c/p\u003e\n\u003cp\u003e3.1.7 Deep Learning Models\u003c/p\u003e\n\u003cp\u003eDeep learning models, such as DBNs and Autoencoders, provide powerful means of extracting complex patterns in large-scale clinical data. DBNs are stacked restricted Boltzmann machines; they are mainly used for pretraining in an unsupervised manner before engaging in a task that requires supervision. The autoencoders are successful in terms of dimension reduction and feature extraction, with the help of which high-dimensional data patterns can be picked up and identified. These models are especially useful in dealing with heterogeneous and multi-dimensional datasets related to sepsis. However, they do require a lot of computing power and tend to be considered \u0026quot;black-box\u0026quot; models, which may affect their adoption in clinical environments.\u003c/p\u003e\n\u003cp\u003e3.1.8 SHAP (Shapley Additive Explanations)\u003c/p\u003e\n\u003cp\u003eAlthough these methods are accurate, they are computationally expensive and less interpretable than single models. It\u0026apos;s a model-agnostic explainable tool that uses SHAP, which attributes the contribution of each feature to the outcome. It solves the issue of interpretability with complex ML models by giving a more qualitative interpretation of the influences on prediction from individual variables such as vital signs or biomarkers. SHAP enhances transparency and clinician trust and would be a valuable addition to sepsis prediction frameworks. However, it can be computationally expensive when applied to large datasets with numerous features.\u003c/p\u003e\n\u003cp\u003eThese models, each with unique strengths and limitations, contribute significantly to advancing sepsis detection and prediction, enabling more accurate diagnoses and timely interventions to improve patient outcomes.\u003c/p\u003e\n\u003cp\u003eFor instance, Decision Trees (including Random Forests) are favored for their interpretability and ability to handle nonlinear relationships, crucial for identifying complex sepsis indicators [83, 37]. Support Vector Machines (SVMs) prove effective in high-dimensional spaces, making them beneficial for datasets with numerous parameters [85, 86]. Logistic Regression offers a statistical approach to predicting binary outcomes, making it suitable for initial sepsis prognosis [87]. Gradient Boosting Machines are capable of handling missing data and improving prediction accuracy through ensemble learning [88, 67]. Neural Networks (CNN \u0026amp; RNN) are employed for their ability to capture temporal patterns in patient data, vital for tracking sepsis progression [89, 35].\u003c/p\u003e\n\u003cp\u003eSome studies also showed a trend towards using Ensemble Methods, which combine multiple machine learning models to improve prediction accuracy and stability, essential for reliable sepsis detection in clinical settings [90]. In addition, deep learning (a subset of machine learning) has become increasingly popular, with models such as deep belief networks (DBNs) and autoencoders excelling at extracting complex representations from multifaceted sepsis-related data [91]. This points out the continuous development and application of sophisticated computational tools to improve sepsis diagnosis and prediction.\u003c/p\u003e\n\u003cp\u003eTremendous diversity and growth have been evident in the research field utilizing machine learning and deep learning for sepsis prediction. The gradual increase of the range of models employed and the research output during the last years has confirmed the relevance and potential of such advanced computational techniques to enhance the prognosis and management of sepsis, and thus enhance clinical decision-making, as well as to reduce the morbidity and mortality due to this disease. [100, 101].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.2 Features and Variables Utilized in Sepsis Detection\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis subsection represents the specific features and variables used in the included studies for sepsis detection and prediction. The quality and specificity of the data sent to the healthcare field are significantly dependent on the effective application of machine learning and deep learning models in healthcare. In the context of sepsis prediction, the selection of relevant features and reliable data sources is critical to developing accurate, reliable, and clinically applicable prediction models [96, 97].\u003c/p\u003e\n\u003cp\u003eThe reviews undertaken have suggested the use of features and variables, showing that sepsis is a complex, multi-factorial process. Features in general can be classified under these headings:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003ePhysiological variables: These are such features as heart rate, respiratory rate, body temperature, blood pressure, and oxygen saturation.\u003c/li\u003e\n \u003cli\u003eLaboratory findings: General laboratory findings include leucocyte count, thrombocyte count, creatinine, bilirubin, lactate, and blood gas, among others\u003c/li\u003e\n \u003cli\u003eClinical findings: History of previous medical conditions, drug intake, invasive devices in place, and documented signs of infection and systemic inflammatory response syndrome.\u003c/li\u003e\n \u003cli\u003eDemographic factors: Age, sex, ethnic group, as these parameters affect the risk of development of sepsis as well as outcomes.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo understand the methodologies and feature engineering techniques that have been used in the different studies on sepsis prediction, we have provided an overview of the machine learning models used in the studies. The following table 3. Breaks down these models in detail and the features considered in each study.\u003c/p\u003e\n\u003cp\u003eTable 3: Sample Table for Machine Learning Models and Feature Sets for Sepsis Prediction\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"634\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eModel Used\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eFeatures and Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[27]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eRandom Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eBlending model baes on Random Forest (RF), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Neural Networks (NN), Decision Trees (DT), Gradient Boosting Machines (GBM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[29]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eRandom Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eSupport Vector Machine (SVM), Logistic Regression (LR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eClinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eLinear Regression, Neural Networks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eClinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eRegression Algorithms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eLaboratory results\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eXAutoNet, Convolutional Neural Network (CNN)-based Autoencoder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eDemographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[35]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eXGBoost, Light GBM, Random Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eDeep learning model: Multi-output Gaussian Process and Recurrent Neural Network (MGP-RNN).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eConvolutional Neural Network (CNN) and Random Forest (RF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eEnsemble of Support Vector Machine, Random Forest, Na\u0026iuml;ve Bayes, Logistic Regression, and Xtreme Gradient Boost.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003elogistic Regression, Random Forest, or Support Vector Machine (SVM).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[53]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eeXtreme Gradient Boosting (XGBoost)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eLASSO, Random Forest (RF), Gradient Boosting Machine (GBM), and Logistic Regression (LR).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[62]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eConvolutional neural networks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003elogistic regression, naive Bayes, SVM, KNN, Gaussian process, random forest, AdaBoost, gradient boosting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[73]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eLasso Regression, SHAP, ML methods (XG Boost, Random Forest, Naive Bayes, Logistic Regression, SVM, KNN, Decision Tree)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eDeep Learning Model: COMPOSER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[84]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eeXtreme Gradient Boosting (XGBoost)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e[88]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 345px;\"\u003e\n \u003cp\u003eGradient Boosting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePhysiological variables, Laboratory results, Clinical data, Demographic information\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFigure 7 depicts the number of research focuses on different categories of features. The x-axis is \u0026quot;Features,\u0026quot; \u0026quot;Clinical Data,\u0026quot; \u0026quot;Demographic Info,\u0026quot; \u0026quot;Laboratory Results,\u0026quot; \u0026quot;Physiological Variables,\u0026quot; and \u0026quot;Others.\u0026quot; The y-axis represents the number of papers concentrating on each category. It can be seen from the graph that \u0026quot;Physiological Variables\u0026quot; received the maximum attention with eight papers, and the second maximum was for \u0026quot;Clinical Data\u0026quot; with four. The papers for \u0026quot;Laboratory Results\u0026quot; and \u0026quot;Demographic Info\u0026quot; were relatively low in numbers. This visualization gives a summary view of the research trend in the specific dataset or study.\u003c/p\u003e\n\u003cp\u003eFeatures and data sources for use in sepsis detection models based on machine learning and deep learning models differ, as the nature of this condition is complex with a multi-layered appearance. Careful selection and incorporation of relevant data points plus the use of comprehensive, timely, and accurate data sources for models that are not only performance-enhancing but also clinically significant and easily integrated into ongoing health systems [99].\u003c/p\u003e\n\u003cp\u003e3.3 Sepsis Detection and prediction techniques\u003c/p\u003e\n\u003cp\u003eThe detection and prediction of sepsis have greatly evolved with the adoption of machine-learning techniques. These methods incorporate a wide range of approaches, including data integration, real-time applications, and robust evaluation metrics, for improving diagnostic precision and timely interventions. A technique in this context refers to a specific methodological approach or computational strategy applied to tackle distinct challenges in sepsis detection. The aim of using such methods is to enhance early diagnosis, ensure the reliability of the model, integrate multiple sources of data, and provide actionable insights to clinicians, ultimately enhancing patient care and clinical outcomes. Following are the comprehensive ten key techniques employed in sepsis detection and prediction, along with their associated references and detailed descriptions.\u003c/p\u003e\n\u003cp\u003e3.3.1 Highlighting Heterogeneity in Approaches\u003c/p\u003e\n\u003cp\u003eSepsis presents diverse clinical manifestations, influenced by factors such as age, infection types, and comorbidities. Predictive tools like SHAP (SHapley Additive exPlanations) facilitate the identification of key features impacting outcomes, such as lactate levels and inflammatory markers [32,73]. Additionally, incorporating patient-reported data addresses variability, enabling more tailored and accurate risk assessments.\u003c/p\u003e\n\u003cp\u003e3.3.2 Comparative Evaluation of Models\u003c/p\u003e\n\u003cp\u003eThe complexity of sepsis often surpasses the capabilities of single predictive models. Ensemble models, like Random Forests and Gradient Boosting Machines, combine multiple algorithms to improve performance and reliability [28, 29]. These methods effectively capture non-linear relationships and interactions among sepsis biomarkers, outperforming simpler models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.3.3 Integration of Data Types\u003c/p\u003e\n\u003cp\u003eIntegrating structured data (lab findings, vital signs) and unstructured data (clinical notes, imaging scans) improves the predictive model. Models created using information from many types of data stand a better chance of predicting the risk of developing sepsis, or its worsening [36, 75]. This integration in its entirety depicts patients\u0026rsquo; conditions from multiple perspectives, thus aiding in enhanced decisions at the clinical level.\u003c/p\u003e\n\u003cp\u003e3.3.4 Application to Specific Clinical Scenarios\u003c/p\u003e\n\u003cp\u003ePersonalized predictive models for specific populations are more applicable and accurate. For example, models of sepsis in neonatal babies revolve around different physiological constants whereas models for sepsis in immunocompromised patients consider different parameters [36, 67]. Custom-made algorithms enable more specific and effective targets for the interventions.\u003c/p\u003e\n\u003cp\u003e3.3.5 Temporal and Internal Validation\u003c/p\u003e\n\u003cp\u003eExternal validation and temporal validation are important in building consistent and applicable models. Testing for sepsis should include assessment based on the stage of the disease starting from the first diagnosis to the last stage. Validation done in time makes it clear that models would be statistics refraining from changing places with time, irrespective of the advancement in clinical settings [35, 53].\u003c/p\u003e\n\u003cp\u003e3.3.6 Performance Metrics for Evaluation\u003c/p\u003e\n\u003cp\u003eSome key performance indicators such as AUC (area under the curve), sensitivity, and accuracy have a very crucial role in the determination of model use performance [27, 88]. When sensitivity is high, the risk of obtaining a false negative score is very small, thereby ensuring.\u003c/p\u003e\n\u003cp\u003e3.3.7 Timeliness and Real-Time Applications\u003c/p\u003e\n\u003cp\u003eSealth care organizations\u0026apos; tools for quick detection and diagnosis are fundamental to the management of sealer infection. Predictive algorithms embedded in hospital information systems make up alerts that give patients an ultimatum based on how their vitals are performing so that hints on treatment decisions can be made promptly [33,75]. Such technologies do drastically cut down the lengths of time it takes seeking interventions improving the chances of survival of the patient.\u003c/p\u003e\n\u003cp\u003e3.3.8 Experimental Evidence Presentation\u003c/p\u003e\n\u003cp\u003eThe experimental proof shows the possibilities of the sepsis prediction models. Tough testing on patient databases improves the strings of detection being early and timely, predictive efficacy, and dependable models [28, 35]. Seeing that they can perform equally well in multiple diverse populations builds great trust in these advanced models.\u003c/p\u003e\n\u003cp\u003e3.3.9 Economic and Clinical Impact Assessment\u003c/p\u003e\n\u003cp\u003eIf the sepsis model is at its best, then hospitals would be best to follow it as it could help reduce overall hospital costs as well as make the best use of living by reducing serious key entries for injured patients [39,75]. Models put in place will also enable patients to adhere to clinical procedures increase death rates and decrease the number of burdens for treating patients. Measuring those effects will support the need for specific technologies for prediction purposes in healthcare systems.\u003c/p\u003e\n\u003cp\u003e3.3.10 Tabular and Visual Summarization\u003c/p\u003e\n\u003cp\u003eGraphical representations such as heatmaps and time-series graphs also facilitate the interpretation of finer details of sepsis data overload. Clinicians can pinpoint.\u003c/p\u003e\n\u003cp\u003e3.4. Evaluating ML-Driven Solutions and Practical Advancements in Sepsis Prediction\u003c/p\u003e\n\u003cp\u003eThis section evaluates the impact of machine learning (ML) technologies on sepsis detection and prediction in light of clinical needs. By providing information from multiple sources such as EMR, vital signs, and demographic data, it has been shown that ML models have great potential in achieving sepsis predictions in a very timely manner and with great accuracy.\u003c/p\u003e\n\u003cp\u003eFor instance, the \u0026lsquo;NAVOY Sepsis\u0026rsquo; prediction algorithm which is developed and externally validated seeks to predict the chance of a patient developing Sepsis in ICU which proves its efficacy in being a predictive model for early treatment. Likewise, a model used in emergency departments was superior in the accuracy of predicting sepsis detection than current early warning score systems [42-44]. These advances serve to illustrate how ML methods can be useful for revolutionizing the detection of sepsis in terms of the time and accuracy of the predictions.\u003c/p\u003e\n\u003cp\u003eIn addition, it has been recommended to use a multi-approach to improve the incorporation and use of ML for early sepsis detection [45]. Another significant advancement concerns an ensemble model that did not only combine LightGBM, XGBoost, and Random Forest but also relied on hourly patient data to surpass other tree models in terms of predictive performance [46]. These examples combined confirm that there are real-world improvements and usefulness of ML-based systems in the clinical management of sepsis patients.\u003c/p\u003e\n\u003cp\u003e3.5. Feature Engineering for Sepsis Detection\u003c/p\u003e\n\u003cp\u003eTo be effective in predictive modeling, it is very important to emphasize feature engineering or a corresponding task that would include the transformation of the raw data into more useful features. For example, concerning sepsis detection, feature engineering involves defining and querying the specific clinical features from structured clinical databases such as EHR, laboratory results, and vital signs. By targeting the best predictors even at the early stage of model building such as placing the most effective features, it is possible to increase the efficiency of the models aimed at predicting the occurrence of sepsis. The model-building process also entails dealing with issues like data absence, overfitting of the model, and feature death to improve the model. This literature review discusses the state of the art of feature engineering as applied to sepsis detection including reviewing the most important studies on feature selection and feature extraction techniques for sepsis prediction. Following are the key points discussed in this review regarding the role of feature engineering in sepsis detection:\u003c/p\u003e\n\u003cp\u003e3.5.1. Feature Selection and Extraction for Sepsis Detection\u003c/p\u003e\n\u003cp\u003eFeature selection and extraction play an important role in selecting the most relevant medical indicators for predicting sepsis. The studies stress selecting clinical input features that play a crucial role in determining the outcome of a patient and understanding the multifactorial causes of the syndrome. The proper extraction of these features increases the detection accuracy of sepsis [47, 49].\u003c/p\u003e\n\u003cp\u003e3.5.2. Importance of Timely Sepsis Prediction\u003c/p\u003e\n\u003cp\u003eThe timely intervention of sepsis can be done with early prediction. Machine learning models like XGBoost have proved to be accurate predictors of sepsis within the first 6 hours of its onset. This suggests that machine learning and deep learning models must be implemented to ensure early detection and effective treatment [48, 50].\u003c/p\u003e\n\u003cp\u003e3.5.3. Handling Missing Data and Overfitting\u003c/p\u003e\n\u003cp\u003eMissing data handling and avoidance of overfitting play important roles in enhancing the precision and generalizability of a machine-learning model. The techniques used here, for feature replacement or selection are mean imputation and chi-square, which help maintain the robustness and reliability of the model while trying to predict sepsis [51].\u003c/p\u003e\n\u003cp\u003e3.5.4. Model Interpretation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterpretability tools in machine learning, such as SHAP (SHapley Additive exPlanations) explain the reasoning behind certain predictions made by a model. SHAP helps reveal why a particular feature results in a model classifying a patient as sepsis. This adds explanatory power to models and guides clinical decision-making [52].\u003c/p\u003e\n\u003cp\u003e3.5.5. Predictive Accuracy of Machine Learning Models\u003c/p\u003e\n\u003cp\u003eXGBoost has demonstrated the highest accuracy of prediction in sepsis detection. Models like LSTM and SVM have shown their potential to be good in the detection of sepsis. These models work with high accuracy based on feature selection and extraction to enhance the accuracy of the predictions [53].\u003c/p\u003e\n\u003cp\u003e3.5.6. Reducing Feature Redundancy\u003c/p\u003e\n\u003cp\u003eFeature redundancy reduction is another efficient strategy to improve the performance of a model. Methods have been presented to remove redundant features from the data and extract strong, meaningful features from large dimensional data. This enables sepsis detection models to predict more accurately and reliably [56].\u003c/p\u003e\n\u003cp\u003e3.5.7. Feature Engineering Across Models\u003c/p\u003e\n\u003cp\u003eFeature engineering plays a vital role in different models for sepsis detection using machine learning. Choosing the correct features, extracting relevant data, and removing redundancy are the essential steps in creating an accurate model. Integration of all these strategies helps improve the performance in the prediction of the onset of sepsis [47, 49 and 53]\u003c/p\u003e\n\u003cp\u003e3.6. Overview of Previous Study\u003c/p\u003e\n\u003cp\u003eMachine learning has brought about a revolution in healthcare in the prediction and management of sepsis, an extremely dangerous condition that necessitates immediate and accurate interventions. The studies are divided into five categories based on their objectives: early detection, mortality prediction, improved interpretability and feature selection, specialized clinical challenges, and evaluation of existing models. The early detection model focuses on models like Random Forest and deep learning methods that predict sepsis onset in the ICU for timely interventions. The study of mortality prediction applies models such as XGBoost and Random Forest for estimating hospital mortality risks and in clinical decision-making. It aims to enhance interpretability and feature selection, leveraging tools like SHAP with refined feature engineering to gain better performance.\u003c/p\u003e\n\u003cp\u003eThe specialized clinical challenges group focuses on ML applications in unique patient populations, for example, hematopoietic cell transplantation. Meanwhile, the evaluation frameworks group reviews existing models and tries to identify areas that could be improved. Thus, by categorizing these studies, we get a clearer view of the varied ways in which ML is developing sepsis prediction and management, providing insight for future research and real-world clinical applications.\u003c/p\u003e\n\u003cp\u003e3.6.1. Early Detection and Prediction of Sepsis in ICU\u003c/p\u003e\n\u003cp\u003eThis team of researchers is developing the early detection of sepsis using machine learning models in ICU settings. All the studies focus on high accuracy and early intervention. For example, one research [27] utilized the Random Forest model, resulting in an AUC of 0.94 to improve the prediction of sepsis mortality in patients within the ICU. Likewise, another research [34] used XAutoNet and CNN-based Autoencoder that showed public health benefits through early detection. In addition, the ensemble approach using XGBoost, Light GBM, and Random Forest was utilized [35] which provided improvements in accuracy to six hours before the diagnosis of a clinical disease. The MGP-RNN deep learning model was studied in a multi-center approach [36], and therefore, provided an excellent evaluation of early sepsis detection. Finally, the NAVOY Sepsis algorithm using CNNs yielded high AUROC scores in the early predictions for the European ICU environment [62].\u003c/p\u003e\n\u003cp\u003e3.6.2. Mortality Prediction in Sepsis Patients\u003c/p\u003e\n\u003cp\u003eThis category focuses on estimating mortality in sepsis patients and sometimes applies biomarkers and clinical measurements to inform the selection of treatment. In this section, [28] created a new blending model composed of RF, XGBoost, LR, SVM, and more to learn interesting temporal correlations related to hospital mortality. Correspondingly, [58] took advantage of LASSO, RF, GBM, and Logistic Regression Models to predict ICU mortality related to patient risk by using metrics such as the Brier score and AUROC. Another work [84] obtained an AUC of 0.94 and an F1 score of 0.937 using XGBoost for in-hospital mortality prediction. Finally, [88] proposed a gradient-boosting model for emergency department triage, which boosted the classification accuracy to 97.67%, further optimizing resource allocation.\u003c/p\u003e\n\u003cp\u003e3.6.3. Improving ML Interpretability and Feature Selection\u003c/p\u003e\n\u003cp\u003eStudies in this category involve making machine learning models interpretable and feature selection for improved performance. For instance, [53] applied XGBoost with SHAP analysis that provided interpretable results and outperformed other models such as MGP-RNN in prognosis prediction. Another study [52] used Logistic Regression, Random Forest, and SVM, combining modified chi-square feature selection to improve both recall and precision for the detection of early sepsis. A study [67] tested ML algorithms, such as Logistic Regression, SVM, and Random Forest, on various datasets and emphasized the feature importance and generalizability of models.\u003c/p\u003e\n\u003cp\u003e3.6.4. Advanced ML Techniques for Specialized Scenarios\u003c/p\u003e\n\u003cp\u003eThis group deals with advanced techniques of machine learning for particular clinical challenges. One article [31] applied SVM and Logistic Regression to predict the HPV type in cervical cancer patients; it had a very structured approach to extracting and analyzing data. Another study [37] was on CNNs and Random Forest models to predict outcomes of sepsis in patients undergoing hematopoietic cell transplantation. Moreover, [73] introduced SERA, an algorithm combining both structured and unstructured data that employs methods including Lasso, SHAP, and XGBoost to mitigate false positives with early detection of sepsis.\u003c/p\u003e\n\u003cp\u003e3.6.5. Evaluation Frameworks and Scoping Reviews\u003c/p\u003e\n\u003cp\u003eThese studies review and comment on existing machine learning models with insights and recommendations for further development. For example, [32] tested the ML models, which included Linear Regression and Neural Networks, in predicting neurodevelopmental outcomes, targeting quality improvement based on patient-reported data. Another related study [33] reviewed the regression-based ML model with a systematic approach that revealed the potential of unstructured clinical text in facilitating the early detection of sepsis. Finally, the COMPOSER deep learning model [75] was tested for its effect on clinical outcomes and resulted in a 1.9% reduction in mortality rates due to sepsis and improved compliance with care bundles for sepsis.\u003c/p\u003e\n\u003cp\u003eThis structured approach organizes studies based on their common purposes and contributions, giving me a clearer understanding of the contribution, they have to the subject of impacts on sepsis-related machine learning.\u003c/p\u003e\n\u003cp\u003eThe table below gives an overview of previous studies that have applied machine learning techniques to the prediction and management of sepsis. It groups the studies according to their objectives: early detection of sepsis, mortality prediction, model interpretability, and specialized clinical applications. By structuring these studies, the table provides an overview of the types of machine learning models used, including Random Forest, XGBoost, CNNs, and ensemble methods, as well as key findings and contributions from each study. This overview can be considered as a resource for understanding the current state of ML applications in sepsis care and helps identify trends, strengths, and areas to be explored further.\u003c/p\u003e\n\u003cp\u003e3.7 Background\u003c/p\u003e\n\u003cp\u003eLi et al. [102] discussed the use of AI in sepsis care, particularly in the early detection and personalized treatment approaches. They also focused on the real-time monitoring of patient data to enhance clinical outcomes. Their review also emphasized the integration of predictive models into clinical workflows. The review study pointed out the challenges concerning adapting AI tools to different healthcare systems. The future directions include developing more interpretable AI models for clinicians. O\u0026apos;Reilly et al. [103] discussed optimizing AI tools in sepsis management, commenting on their utility for early diagnosis and treatment planning. The authors suggested multidisciplinary collaboration as the key to the effective use of AI. Identified barriers included algorithm bias and issues with data standardization. They provided an overview of the current state and proposed strategies for overcoming challenges associated with integrating AI. The main focus of their investigation was related to patient outcomes and possible AI contributions to these outcomes. Asif et al. [104] investigated machine learning progress in the field of medical diagnostics, specifically focusing on the potential to increase diagnostic accuracy. It explored several algorithms and their use cases in various fields of healthcare, including sepsis. Challenges such as a lack of available data and a requirement for high-confidence validation frameworks were identified. It pointed out that for successful implementation, collaboration between the clinician and AI is needed. Future studies should focus on ethical and technical issues. Yang et al. [105] recently reviewed the use of AI applications in the management of sepsis. There has been growth regarding the predictive accuracy of risks as well as decreased mortality with their application. Machine learning models discussed include those concerning clinical validation. High-quality datasets are crucial in training robust algorithms, and AI tool integration into healthcare systems represents a major challenge. According to them, the integration of AI could revolutionize sepsis management by improving patient outcomes. Rashid et al. [106] studied the use of gene expression-based machine-learning models to enhance the diagnosis and treatment of sepsis. They discussed genetic biomarkers as potential predictors with increased accuracy for better prediction. The authors further proposed the incorporation of multi-modal data to help in refining the decision-making process clinically. It stressed the idea of personalized AI-guided treatment plans. It discussed heterogeneity in data and interpretability as challenges.\u003c/p\u003e\n\u003cp\u003eGorecki et al. [107] highlighted the potential role of AI in the diagnosis of sepsis and septic shock, noting the ability to examine complex clinical data. A review was carried out regarding the performance of AI models across various healthcare settings. Data integration and standardization emerged as key barriers. Collaborative efforts by AI developers and clinicians were proposed to implement better systems. Future directions are focused on the fine-tuning of models to allow for real-time decision-making. Gao et al. [108] illustrated the utilization of machine learning in predicting ICU patient mortality by sepsis with clinical biomarkers. This research had promising high accuracy, but the work would be quite appropriate for a real-time environment. They indicated the need for much larger datasets in validating the model. Their challenges included the bias in algorithm and data preprocessing. Their findings did show a promise for an ML revolution in critical care. Padhi et al. [109] discussed AI applications in clinical virology, primarily on machine learning and deep learning. It explained how those technologies would be transformed to diagnose sepsis complications. The challenge in adopting AI was identified as data quality and model transparency. They proposed combining AI with molecular biology for better outcomes. The review also highlighted the necessity of interdisciplinary research.\u003c/p\u003e\n\u003cp\u003eHamid et al. [110] introduced a machine-learning model that was used for brain tumor detection from MRI images. The precision of the model was emphasized, although it had nothing to do with sepsis. They illustrated the diversity of AI in health care and touched upon difficulties related to preprocessing and scalability. Results indicated that developers of AI should collaborate more with clinicians. Further work needs to be conducted to improve the interpretability of models. Cheungpasitporn et al. [111] analyzed AI in the diagnosis of sepsis-related AKI and concluded that early detection is achievable through AI. AI can be employed for the interpretation of biomarkers for improved risk stratification. Challenges in integrating AI into the clinical workflow are a challenge, as well as issues of bias of the algorithm and lack of data standardization. Bignami et al. [112] provided an overview of AI applications in sepsis management for clinicians, noting advances in predictive modeling. Real-time analytics can be important in improving care for patients. The challenges identified include data privacy and the necessity of cross-institutional validation. The study further underlined the role of interdisciplinary collaboration in the refinement of AI systems. The future directions included integrating AI tools with electronic health records (EHRs).\u003c/p\u003e\n\u003cp\u003eWu et al. [113] compared logistic regression with machine learning for predicting mortality in adult sepsis patients. The authors showed that the machine learning model performed better when dealing with complex datasets. Srivastava et al. [114] discussed how AI could potentially be used early in the identification and treatment process of infectious diseases, such as sepsis. The authors acknowledged improvements in diagnosis through machine learning and deep learning-powered diagnostic aids. Issues that arose included standardized data and a lack of model generalization. The importance of real-time deployment of models was stressed in the study as well as emphasizing ethical AI design.Shayegh and Tadj [115] applied deep audio features along with self-supervised learning for early detection of neonatal sepsis. They proposed a model that analyzed infant cry signals to classify the diseases with great accuracy. However, they recommended that bigger datasets would enhance robustness. Other problems that are in their discussions were data variability and signal noise. Future work can be proposed for the integration of their model in neonatal care systems.\u003c/p\u003e\n\u003cp\u003eKombo et al. [116] suggested the development of an electronic nose using machine learning to diagnose neonatal sepsis from volatile organic compounds in fecal samples. It shows great potential in the early stages of the disease. The challenges presented include standardization of biomarkers and data acquisition. It emphasized integration into clinical settings as a step of prime importance. The areas of improvement involve the enhancement of sensor technology and sensitivity of the model [117]. Yeo et al. [118] explored the obesity paradox in sepsis and its long-term outcomes, underlining the differential effects of body composition. The study applied machine learning to patient data to look for trends. The diversity of data and lack of generalizability across populations were challenges [119]. Yong and Zhenzhou [120] applied deep learning for in-hospital mortality prediction for sepsis patients, with high predictive accuracy. Feature engineering was discussed in terms of its facilitative role in improving model performance. Challenges such as overfitting and a paucity of generalizability were posed. The importance of seeking AI integration into clinical workflows is emphasized in the study. Future directions encompass developing more robust validation frameworks.\u003c/p\u003e\n\u003cp\u003eBoussina et al. [121] evaluated how deep learning-based sepsis prediction models affected the quality of care and survival. They showed importance in the aspects of earlier diagnosis and better patient outcomes. Challenges from real-time deployment and data standardization were reported. The authors also underlined the need for clinician involvement in developing AI. Scaling such models across healthcare systems should be considered for further research [122]. P\u0026eacute;rez-Tome et al. [123] employed machine learning for the mortality of sepsis, demonstrating very high accuracy on clinical data. The research discussed feature selection and how this plays a major role in bettering the performance of the model. Difficulties, such as preprocessing of data, and ethical dilemmas, were examined. An urgent priority to the integration of AI tools in the clinical arena is indicated. Algorithms will need refinement for future use in real-time applications. Zhang et al. [124] conducted a multicenter study applying machine learning to predict in-hospital mortality in patients with sepsis. The authors demonstrated that the integration of clinical and inflammatory biomarkers results in higher predictive accuracy. Data heterogeneity, validation, and other issues related to this approach were discussed. The paper indicated the importance of cross-institutional collaborations in future work: generalizability of the models should be enhanced [125].\u003c/p\u003e\n\u003cp\u003eTable 4:\u0026nbsp;Comparative Analysis of Sample Literature\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"546\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRef.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTitle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eML Model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey Findings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[27]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003ePrediction of sepsis mortality in ICU patients using machine learning methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003edevelop an ML model to improve sepsis prediction accuracy using a reduced set of features and enhance model interpretability.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eRandom Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eXGBoost model achieved an AUC of 0.94 and an F1 score of 0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eDevelopment and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003edevelop a novel blending ML model for predicting hospital mortality in ICU patients with sepsis, improving the score of SAPS II and SOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eBlending model based on RF, XGBoost, LR, SVM, k-NN, NB, NN, DT, GBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eInsightful temporal associations in sepsis prediction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[29]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eA machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eidentify diagnostic variables for sepsis at the ED using machine learning models trained with high-quality classifications assigned by experts.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eRandom Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eRandom Forest 97.55% sensitivity and 97.3% AUC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003ePerformance analysis of class imbalance handling techniques for early sepsis prediction using machine learning algorithms\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo evaluate and compare three class imbalance handling techniques in medical datasets to improve machine learning-based sepsis prediction, and to develop a novel predictive model using only healthcare parameters available at peripheral basic health facilities (without requiring lab investigations).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eSepsis Prediction Model for Peripheral Hospitals (SPMPH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThe SPMPH model outperformed existing models, achieving: Accuracy: 0.95, Precision: 0.98, Recall: 0.91, AUROC: 0.978\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003ePrediction of carcinogenic human papillomavirus types in cervical cancer from multiparametric magnetic resonance images with machine learning-based radiomics models. Diagnostic and interventional radiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eassess ML-based radiomics models for predicting carcinogenic HPV types from pre-treatment MRI features.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eSupport Vector Machine (SVM), Logistic Regression (LR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eMethodical approach in data extraction and analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eMachine Learning Prediction Models for Neurodevelopmental Outcome After Preterm Birth: A Scoping Review and New Machine Learning Evaluation Framework\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003ereview ML models for predicting neurodevelopmental outcomes in infants and assess quality for future improvements.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eLinear Regression, Neural Networks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eInnovative use of patient-reported data in sepsis prediction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eSepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eevaluate the impact of using unstructured clinical data in ML for the early detection, prediction, and identification of sepsis, compared to structured data.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eRegression Algorithms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eLimited by data scope but offers specific insights\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eEarly prediction of sepsis using machine learning. In 2021 11th International Conference on Cloud Computing, Data Science \u0026amp; Engineering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003edevelop a classifier for early sepsis detection up to six hours before clinical diagnosis using patient data and evaluate ML models for accurate prediction.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eXAutoNet, Convolutional Neural Network (CNN)-based Autoencoder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eBroad public health implications in sepsis detection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[35]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eAn ensemble machine learning model for the early detection of sepsis from clinical data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eEstablish an ensemble model for early identification of sepsis, 6 hours before clinical diagnosis, utilizing ICU patient records and comparing it with single models to enhance prediction accuracy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eXGBoost, Light GBM, Random Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eExperimental but provides foundational knowledge.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eMachine learning for early detection of sepsis: an internal and temporal validation study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eevaluates how deep learning models detect sepsis earlier and more accurately than other methods, using clinical data.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eDeep learning model: Multi-output Gaussian Process and Recurrent Neural Network (MGP-RNN).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eComprehensive analysis with multi-center data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eA Machine-Learning Sepsis Prediction Model for Patients Undergoing Hematopoietic Cell Transplantation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003edevelop an ML-based sepsis prediction model for hematopoietic cell transplantation (HCT) patients, using EHR to identify high-risk patients and improve early detection and outcomes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eConvolutional Neural Network and Random Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003ePreliminary findings with potential for future research\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eA machine learning model for early prediction and detection of sepsis in intensive care unit patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003epropose a machine learning model for early sepsis detection in ICU patients, comparing the performance of various models to improve accuracy and reduce mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eensemble of SVM, RF, NB, LR, and XGBoost.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eOutperforms an accuracy of 0.96, demonstrating early sepsis detection in ICU patients.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eEE550 The Potential Cost and Cost-Effectiveness Impact of Using Machine Learning Sepsis Prediction Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden and the United Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo anticipate both the budget savings and improvements for patients that a three-hour-ahead sepsis-prediction tool could offer in ICU units in Sweden and the United Kingdom.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eSupervised Machine Learning Classifier\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThey suggest that putting this sepsis prediction strategy in place can help save lives and lower costs in the ICU.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eSepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo evaluate the impact of using unstructured clinical text combined with machine learning (ML) and natural language processing (NLP) techniques on the prediction, early detection, and identification of sepsis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eML and NLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eCombining unstructured clinical text with structured data improves the accuracy and timing of sepsis prediction compared to using structured data alone, as evidenced by better AUC (Area Under the ROC Curve) scores.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eEarly Prediction of Sepsis Based on Machine Learning Algorithm\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo predict early sepsis 6 hours in advance by applying machine learning algorithms with different data processing methods to improve prediction accuracy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eXGBoost and LightGBM algorithms were used. Two processing methods were compared: Mean processing method and Feature generation method.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eBoth XGBoost and LightGBM achieved excellent performance, with AUC ranging from 0.910 to 0.979.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eSupervised machine learning for early predicting the sepsis patient: modified mean imputation and modified chi-square feature selection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eenhance sepsis detection accuracy by using mean imputation and chi-square feature selection techniques, achieving improved performance and reduced processing time.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eLogistic Regression, Random Forest, or Support Vector Machine (SVM).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eUsed modified mean-imputation and Chi-square for feature selection, focused on accuracy and recall, precision, and f1 score metrics.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[53]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eInterpretable machine learning for early prediction of prognosis in sepsis: a discovery and validation study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003edevelop and validate an interpretable ML model for predicting mortality in sepsis patients, using clinical features and SHAP for model interpretability.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eeXtreme Gradient Boosting (XGBoost)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003edetection of sepsis using a multivariate time series of physiological measurements in ICU patients. Outperformed MGP-RNN.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eMchine learingfor the prediction of acute kidney injury in patients with sepsis\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo develop and validate machine learning models for early prediction of acute kidney injury (AKI) in critically ill patients with sepsis, aiming to improve timely intervention and patient prognosis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eSeveral ML algorithms were applied: logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThe XGBoost model achieved the highest predictive performance with an AUC of 0.821, outperforming all other ML models and clinical scores.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[55]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eA review of feature selection methods for machine learning-based disease risk prediction\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo provide a comprehensive overview of feature selection methods in machine learning, focusing on improving disease risk prediction from genotype data by identifying relevant features (SNPs) and addressing the challenges caused by high-dimensional genetic datasets.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003evarious feature selection techniques\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eMachine learning helps detect patterns in large, complex genotype datasets for precision medicine. Feature selection improves model generalizability by removing irrelevant, noisy, or redundant features and retaining only the most informative ones.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eFeature Extraction and Selection in Hidden Layer of Deep Learning Based on Graph Compressive Sensing\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo address feature redundancy in the hidden layers of deep learning models when processing high-dimensional, multi-modal data by proposing a novel feature extraction and selection method.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003efeature extraction and selection method based on graph compressive sensing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eHigh-dimensional, multi-modal data often contain redundant features in deep learning hidden layers. The proposed graph compressive sensing method effectively extracts low-dimensional features while eliminating redundancy.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[59]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eOn classifying sepsis heterogeneity in the ICU: insight using machine learning\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo improve sepsis prediction from electronic health records (EHR) by stratifying ICU patients into clinically significant sepsis subpopulations based on distinct organ dysfunction patterns, addressing the heterogeneity of sepsis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eRandom Forest, Gradient Boost Trees, Support Vector Machines\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eStratifying sepsis patients by organ dysfunction subtypes improved the models\u0026rsquo; ability to distinguish septic from non-septic ICU patients.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eAssessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo investigate the effects of data drift on machine learning models predicting sepsis onset from electronic health records (EHR) and to provide insights on how to monitor and retrain models effectively in changing clinical environments.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eeXtreme Gradient Boosting (XGB), Recurrent Neural Network (RNN)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eMonitoring infrastructure for sepsis prediction may be less demanding compared to other applications with more frequent data drift.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eA comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo develop and validate machine learning models that predict 31-day mortality in patients presenting to the emergency department (ED) with sepsis, and to compare the models\u0026rsquo; performance against internal medicine physicians and established clinical risk scores.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCombination of laboratory and clinical data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThe ML models predicted 31-day mortality with AUCs of 0.82 (lab data only) and 0.84 (lab + clinical data).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[63]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eValidation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48\u0026nbsp;h in advance using a diverse dataset from 461 US hospitals\u003c/p\u003e\n \u003ch3\u003e\u0026nbsp;\u003c/h3\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo develop and validate a machine learning algorithm (MLA) that predicts severe sepsis onset up to 48 hours in advance using readily available electronic health record (EHR) data.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eDiverse datasets from multiple health centers and community hospitals, using vital signs and other EHR data.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThe MLA demonstrated high predictive accuracy with AUROC of 0.931 at sepsis onset and 0.827 at 48 hours before onset on the testing dataset.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[64]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eDevelopment and evaluation of a machine learning model for the early identification of patients at risk for sepsis\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo develop a new machine learning-based sepsis screening tool, called the Risk of Sepsis (RoS) score, and compare its performance against established screening tools such as SIRS, SOFA, qSOFA, MEWS, and NEWS in emergency department patients.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eGradient Boosting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThe RoS score outperformed all benchmark tools across multiple time points (1, 3, 6, 12, and 24 hours) with AUROC between 0.93 and 0.97.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eLiSep LSTM: a machine learning algorithm for early detection of septic shock\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo develop and evaluate \u0026ldquo;LiSep LSTM,\u0026rdquo; a Long Short-Term Memory neural network model for early identification of septic shock in ICU patients.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eLSTM neural network\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eLiSep LSTM outperformed a less complex model using the same features and targets. Achieved an AUROC of 0.8306 (95% CI: 0.8236\u0026ndash;0.8376).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[66]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eEarly detection of sepsis utilizing deep learning on electronic health record event sequences\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo develop a deep learning model for early sepsis detection using a diverse multicenter dataset that overcomes limitations of prior models\u0026mdash;such as limited clinical parameters, ignoring clinical interventions, and focusing only on ICU data\u0026mdash;thereby expanding applicability beyond intensive care units.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eConvolutional Neural Network (CNN) and Long Short-Term Memory (LSTM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThe system learns feature interactions directly from raw data without manual feature extraction, outperforming baseline models.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eEvaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo validate a gradient boosted ensemble machine learning algorithm for early sepsis detection and prediction using electronic health records, and compare its performance with existing clinical scoring systems.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eGradient Boosted ensemble model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThe machine learning algorithm (MLA) achieved AUROC scores of 0.88 at sepsis onset, 0.84 at 24 hours prior, and 0.83 at 48 hours prior to sepsis onset.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eArtificial intelligence, machine learning and deep learning: Potential resources for the infection clinician\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo review recent and potential future applications of artificial intelligence (AI), machine learning (ML), and deep learning in infection research and clinical practice.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eThe review covers a broad range of AI/ML/deep learning applications rather than focusing on a specific model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAI shows promise in multiple infection-related domains but most studies lack real-world clinical validation and utility metrics.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[76]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eThe signature-based model for early detection of sepsis from electronic health records in the intensive care unit\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo develop an automatic, signature-based regression model for predicting a patient\u0026rsquo;s risk of sepsis over time using physiological data streams in the ICU.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eGradient boosting machine (GBM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThe signature-based approach offers a competitive and systematic way to model sepsis risk using streaming health data.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[77]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003ePredicting sepsis in multi-site, multi-national intensive care cohorts using deep learning\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo develop and validate a machine learning system for early prediction of sepsis in ICU patients using a large, multinational, multi-center dataset.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003edeep self-attention model trained on 156,309 ICU admissions from five harmonized ICU databases across three countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThe model predicted sepsis with an average AUROC of 0.847 \u0026plusmn; 0.050 in internal out-of-sample validation and 0.761 \u0026plusmn; 0.052 in external validation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[78]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eImproving Patient Outcomes Through Effective Hospital Administration: A Comprehensive Review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo review the critical role of effective hospital administration in improving patient outcomes by exploring key components, strategies, measurement methods, and future trends in healthcare management.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003ePatient-centered care and interdisciplinary collaboration are vital for better outcomes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eA collective effort by healthcare leaders and policymakers is needed to develop skilled administrators, invest in technology, promote value-based care, and reduce disparities.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[79]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eDigital health data quality issues: systematic review\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo develop a consolidated Digital Health Data Quality (DQ) Dimension and Outcome (DQ-DO) framework that identifies the key dimensions of digital health data quality, their interrelationships, and their impacts.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eAnalyzed 227 peer-reviewed articles focused on digital health data quality in hospital settings.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThe framework highlights the complexity of digital health data quality and aids healthcare executives in prioritizing DQ improvement efforts.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[81]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eEthical issues in biomedical research using electronic health records: a systematic review\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo systematically review and analyze the ethical issues related to the use of electronic health records (EHRs) in research, focusing on challenges in managing access and governance.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eEmployed constant comparative method to identify common ethical themes and summarized empirical studies descriptively.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThe digital transformation of healthcare is reshaping concepts of privacy, beneficence, and ethics in healthcare, research, and public health.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[82]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003eA Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo develop and implement a machine learning algorithm for predicting severe sepsis and septic shock in non-ICU hospital admissions and evaluate its impact on clinical practice and patient outcomes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eRandom Forest Classifier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThe algorithm achieved 26% sensitivity, 98% specificity, 29% positive predictive value, and a positive likelihood ratio of 13.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eUsing machine learning methods to predict in-hospital mortality of sepsis patients in the ICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003edevelop machine learning models to predict in-hospital death risk in ICU sepsis patients, aiding physicians in making timely clinical decisions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eLASSO, Random Forest (RF), Gradient Boosting Machine (GBM), and Logistic Regression (LR).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eEvaluated ICU patients with sepsis, focused on overall performance and discrimination metrics like Brier score and AUROC.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[62]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eA machine learning sepsis prediction algorithm for intended intensive care unit use (NAVOY Sepsis): proof-of-concept study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003edevelop the NAVOY Sepsis machine learning algorithm for early sepsis detection in ICU patients, using routinely collected data for clinical use in European ICUs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eConvolutional neural networks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAimed at early sepsis prediction, high AUROC on training and test data, focused on sensitivity, specificity, and accuracy.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eValidation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003edevelop ML algorithm for predicting sepsis up to 48 hours before onset using patient data, aiming to improve early detection and patient outcomes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003elogistic regression, naive Bayes, SVM, KNN, Gaussian process, random forest, AdaBoost, gradient boosting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFocused on machine learning prediction models for infant sepsis using EHR data.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[73]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eArtificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003edevelop the SERA algorithm, which uses both structured data and unstructured data to predict sepsis, improving early detection and reducing false positives.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eLasso Regression, SHAP, ML methods (XG Boost, Random Forest, Naive Bayes, LR, SVM, KNN, Decision Tree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAimed at predicting in-hospital mortality, used SHAP for feature importance analysis.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eImpact of a deep learning sepsis prediction model on quality of care and survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eevaluate the COMPOSER deep-learning model on sepsis prediction, focusing on its effects on mortality, sepsis bundle, and organ failure.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eDeep Learning Model: COMPOSER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eCOMPOSER model: 1.9% reduction in sepsis mortality and 5% increase in sepsis bundle compliance, 4% reduction in 72-hour SOFA score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[84]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003ePredicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eto develop and validate an interpretable ML model using clinical features and inflammatory biomarkers to predict mortality risk in critically ill sepsis patients.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eeXtreme Gradient Boosting (XGBoost)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eXGBoost model achieved an AUC of 0.94 and an F1 score of 0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[88]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eA gradient boosting machine learning model for predicting early mortality in the emergency department triage: devising a nine-point triage score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eevaluate a ML model for predicting mortality in the emergency department, to improve patient categorization and resource allocation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eGradient Boosting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFocused on increasing sensitivity for sepsis prediction using mean imputation and chi-square feature selection. Achieved 97.67% classification accuracy and improved processing time.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[94]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eEarly detection of sepsis utilizing deep learning on electronic health record event sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo build a sepsis early notice model that surpasses previous models by being applicable outside of ICUs, having access to more data and not needing manual feature engineering.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eA hybrid architecture combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThe predictive ability ranged from 0.856 (24 hours before) to 0.756 (1 hour before) AS onset.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eMachine learning for early detection of sepsis: an internal and temporal validation study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo check if a deep learning model spots sepsis more correctly than standard machine learning and scoring methods, with results that reflect real-world clinical experience.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eRandom Forest (RF), Cox Regression (CR), Penalized Logistic Regression (PLR), Multi-output Gaussian Process combined with a Recurrent Neural Network (MGP\u0026ndash;RNN)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eThe model that uses both machine learning and recurrent neural networks (MGP\u0026ndash;RNN) had a better C-statistic of 0.88 for predicting sepsis within 4 hours than RF (0.836), CR (0.849), PLR (0.822) and clinical scores SIRS (0.756), NEWS (0.619) and qSOFA (0.481).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e[98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eEarly prediction of sepsis in the ICU using machine learning: a systematic review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTo examine and evaluate scientific studies that apply machine learning to predict sepsis in adult intensive care unit (ICU) patients.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eMultiple ML algorithms reviewed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eMost of the methods (86.4%) evaluated using offline data and horizon evaluation; almost no studies considered online training scenarios. It points out that there are major issues in reviews, including a lack of similarity, trouble with reproducibility and many different approaches.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 4. is a compilation of studies focused on machine learning models for sepsis prediction and early detection, especially in ICU and emergency departments. Each study indicates the objectives, machine learning models applied, and the most important findings in terms of accuracy, time of early prediction, and performance metrics of the model. For example, some research studies use models such as XGBoost, Random Forest, and Convolutional Neural Networks to improve the prediction abilities, while others focus on the interpretability of the model, feature selection techniques, and data handling methods for better results. The studies reveal the potential of machine learning in the early detection, mortality prediction, and better patient outcomes in sepsis care.\u003c/p\u003e"},{"header":"4.\tKey Factors Affecting Sepsis Prediction Model Performance ","content":"\u003cp\u003eSepsis prediction models utilize machine learning to analyze clinical data and give early warnings for the development of sepsis. The following points summarize the critical factors that affect their performance and reliability:\u003c/p\u003e\n\u003cp\u003e4.1. Type of Data Used\u003c/p\u003e\n\u003cp\u003eThe type of data used within a sepsis prediction model determines its overall accuracy and reliability. Some studies integrate both structured and unstructured data, including electronic health records, laboratory results, and vital signs, as well as unstructured data, such as clinical notes or free-text data, which may offer more comprehensive views of a patient\u0026apos;s condition [72]. Other models may look specifically at structured data, such as vital signs or lab results, meaning their scope of prediction may be slightly narrowed but can be built to work much faster and possibly even more efficiently. Different data types can also blend well together to improve the outcome of the model because, especially in sepsis, multiple factors are complex.\u003c/p\u003e\n\u003cp\u003e4.2. Generalization to Unseen Data\u003c/p\u003e\n\u003cp\u003eIn summary, sepsis prediction models need to avoid overfitting the training data. Overfitting occurs when a model performs well on the data it was trained on but fails to generalize to new, unseen data [69]. Poor real-world clinical performance can be caused when, outside of training datasets, actual patient data differs. Fundamentally, such a model is robust enough to generate effectively across different populations and diverse settings. Techniques like cross-validation and the use of independent test sets can facilitate proper generation by models, avoiding heavy dependence on specific datasets but instead adapting to diverse clinical environments.\u003c/p\u003e\n\u003cp\u003e4.3. Model Transparency (\u0026quot;Black Box\u0026quot; Nature)\u003c/p\u003e\n\u003cp\u003eQuite several advanced machine learning models, such as deep learning, present a \u0026quot;black box problem\u0026quot; because of the complexity; these models predict correctly; however, in a real clinical setting, such decision-making is not clear, rendering it difficult to have the interpretation of the same. Algorithm transparency is essential to winning over the trust of the clinicians and ensuring the clarity of the model recommendations with clear actionability [70]. Techniques such as SHAP (SHapley Additive exPlanations), or LIME (Local Interpretable Model-agnostic Explanations) could help shed light on what kind of input features make such differences in the model output, which is even a life-or-death issue in healthcare.\u003c/p\u003e\n\u003cp\u003e4.4. Addressing Data Imbalance\u003c/p\u003e\n\u003cp\u003eSepsis is usually a rare occurrence, making datasets used for training predictive models imbalanced, having significantly fewer sepsis events than those of non-sepsis events. Data imbalance can then lead to skewed predictions, where the model may favor the majority class (non-sepsis) and miss out on actual cases of sepsis. Improvement can also be realized in the model\u0026apos;s capacity to correctly classify sepsis cases by applying various data imbalance techniques, like oversampling the minority class of cases (sepsis cases) or under sampling the majority class of cases (non-sepsis cases) [73]. Other data generation techniques, such as SMOTE - Synthetic Minority Over-sampling Technique can generate synthetic data, providing an opportunity for better-balanced training that should result in more precise and accurate prediction models.\u003c/p\u003e\n\u003cp\u003e4.5. Sensitivity to Sepsis Events\u003c/p\u003e\n\u003cp\u003eConsidering that early detection is critical, models must be sensitive to sepsis events. In other words, they should not produce false negatives, or miss a patient who has sepsis. High sensitivity means that the models identify as many of the sepsis cases as possible so that appropriate interventions can be made on time and may save lives. However, sensitivity typically comes with a cost to specificity: correctly identifying that the individual does not have sepsis. And balance needs to be maintained across these measures. Performance metrics of AUC-ROC and precision-recall curves help judge how well a model is classifying cases as either sepsis or a non-sepsis event, maximizing sensitivity [74].\u003c/p\u003e\n\u003cp\u003e4.6. Choice of Performance Metrics\u003c/p\u003e\n\u003cp\u003eThe right selection of performance metrics will help to evaluate the efficacy of the sepsis prediction model. Though useful, the standard metrics include accuracy, sensitivity, specificity, and precision. The metrics fail to cover the clinical significance of the model in detail. For example, the use of precision-recall curves is significant when the case of sepsis is infrequent, as it happens in the detection of sepsis. The area under the curve of precision-recall gives a more meaningful evaluation when dealing with imbalanced data sets [74]. How the performance metrics are in line with clinical goals of minimizing false negatives to help in early intervention and reduce delays in treatment is essential for improving the outcomes of patients.\u003c/p\u003e\n\u003cp\u003eTherefore, based on these key factors, better sepsis predictive models can be developed to be more precise reliable, and clinically relevant enough to improve patient outcomes and reduce mortality from sepsis.\u003c/p\u003e"},{"header":"5.\tMODEL VALIDATION TECHNIQUES","content":"\u003cp\u003eModel Validation Techniques: Machine learning models, especially in healthcare, require comprehensive development and validation to guarantee their reliability and accuracy. Validating a model not only assures that it performs well on the training data but also that it can generalize to unseen real-world data, where the predictions can have serious clinical implications. This process involves several critical steps: preparing high-quality data, choosing appropriate algorithms, performance evaluation, interpretability, and addressing ethical issues. All of these steps are important in developing robust models that could help clinicians make decisions. The following sections detail the most important model validation techniques, providing best practices on how to optimize performance and ensure successful deployment in healthcare settings such as sepsis prediction.\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Data Preprocessing and Feature Engineering: Data quality is ensured by cleaning inconsistencies and normalizing features. Related clinical features relevant to the cases in focus should be brought forward or extracted from varied sources (Electronic Health Records, labs, and vital signs) to allow a very accurate prediction Addressed missing data can then come as second through replacement/selection processes involving Chi-square mean imputation. Redundant features need to be eliminated for better performance of the model, and robust features must be derived from high-dimensional data for better reliability in prediction.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Model Selection and Training: For the proper selection of algorithms suitable for machine learning, the task being processed and the characteristics of data to be handled are essential considerations. Several algorithms were reviewed in the document including Decision Trees, Support Vector Machines, Logistic Regression, Gradient Boosting Machines, Neural Networks, and Ensemble Methods. Special techniques ought to be implemented especially because sepsis onset events are often rare in nature. In addition, one must ensure the ability of the model to generalize for unseen data through cross-validation and overfitting through excessive complexity.\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Model Evaluation and Validation: It is important to check the model using appropriate metrics, such as AUC, sensitivity, specificity, accuracy, and precision-recall curves. The model must be tested on various timelines and disease stages to test the reliability and generalization ability in dynamic clinical settings. The validation should be done on different datasets such as internal and external to validate the generalization capability of the model. Comprehensive testing on diverse patient datasets is required to verify improvements in early detection, prediction accuracy, and overall reliability.\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Interpretability and Explainability: Models or techniques that have interpretability should be selected so that clinicians can understand the rationale behind predictions. For example, tools like SHAP (SHapley Additive exPlanations) can be used to explain how different features contribute to predictions, which adds to the transparency and support for clinical decision-making.\u003c/p\u003e\n\u003cp\u003e5. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Addressing Ethical and Data Privacy: Ethical guidelines should be followed. These include data privacy, patient consent, and the responsible use of data. The development of the model should be such that it adheres to these principles, thereby protecting patient information and trust in the model\u0026apos;s predictions. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"6. Challenges and Limitations of Machine Learning Techniques for Sepsis Detection ","content":"\u003cp\u003eThis paper\u0026apos;s review presents several challenges and limitations that need careful consideration, especially in methodology, data interpretation, and scope of analysis. Challenges facing this paper include availability and quality of data, class imbalance, generalizability, interpretability, clinician trust, and ethical and legal issues. Ethical and legal considerations also arise. Each of these factors holds a crucial position in the reliability and applicability of machine learning models in healthcare, so a balanced approach is needed to overcome these limitations. Discussion of these challenges is as follows.\u003c/p\u003e\n\u003cp\u003e6.1 Data Availability and Quality\u003c/p\u003e\n\u003cp\u003eThe quality and completeness of the data are crucial to the development of reliable ML models. Clinical datasets, which often originate from EHRs, are often characterized by missing or incomplete entries, thereby limiting their utility for training and validating models. Noise and inconsistencies in data further complicate the process of building robust models. Standardizing data collection practices and addressing data gaps are critical to improving model performance [27, 28, 29 and 31].\u003c/p\u003e\n\u003cp\u003e6.2 Class Imbalance and Rare Events\u003c/p\u003e\n\u003cp\u003eSepsis is a rare event compared to other conditions, leading to an imbalance in datasets where septic cases are significantly outnumbered by non-septic cases. This imbalance can bias ML models toward predicting the majority class, reducing their ability to detect true positives. Techniques like oversampling and synthetic data generation have been employed but may lead to overfitting. Balancing datasets remains an ongoing challenge [33, 35, 38, 52 and 62].\u003c/p\u003e\n\u003cp\u003e6.3 Generalizability Across Clinical Settings\u003c/p\u003e\n\u003cp\u003eModels often fail to generalize effectively when applied to new datasets or healthcare settings due to differences in population characteristics, clinical practices, and data collection protocols. This lack of generalizability limits their usability across diverse healthcare environments. External validation using independent datasets is essential to assess robustness but is hindered by privacy concerns and regulatory barriers [36, 37, 38 and 58].\u003c/p\u003e\n\u003cp\u003e6.4 Interpretability and Clinician Trust\u003c/p\u003e\n\u003cp\u003eMany ML models, particularly those based on deep learning, are perceived as \u0026quot;black boxes,\u0026quot; making their predictions difficult for clinicians to understand. This lack of interpretability can erode trust and hinder adoption in clinical practice. Tools like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) help provide insights into feature importance but often fail to address the underlying complexity of the models. Bridging the gap between accuracy and interpretability is vital for clinical acceptance [32, 34, 53 and 67].\u003c/p\u003e\n\u003cp\u003e6.5 Ethical and Legal Concerns\u003c/p\u003e\n\u003cp\u003eThe use of patient data in ML models raises ethical and legal challenges, particularly concerning data privacy, informed consent, and fairness. Regulations like GDPR impose strict compliance requirements for data handling and sharing, which can slow down model development. Additionally, biases in datasets may lead to unfair predictions, exacerbating healthcare disparities. Ensuring ethical integrity and compliance is a critical consideration in ML deployment [73, 75, 84 and 88].\u003c/p\u003e"},{"header":"7. Discussion ","content":"\u003cp\u003eEarly detection of sepsis has a significant impact on patient outcomes. Awareness of sepsis especially at the initial level is vital since it helps in early intercession leading to multiorgan dysfunction and consequently improved patients\u0026rsquo; survival. Due to the complexity of the task, machine learning has been identified as an important tool in this field. Due to the ability to process large volumes of data quickly, sepsis detection systems based on ML give real or early warning signals. It enables the clinicians to intervene before the disease progresses, this is always beneficial in saving lives. These new detection systems have significant clinical applications ranging from shorter ICU stays and corresponding lowered hospital costs to an overall enhancement of the patient\u0026rsquo;s care.\u003c/p\u003e\n\u003cp\u003eIndeed, the definition of sepsis has been changing over time, and any alteration in the definition of sepsis affects machine learning models. Heterogeneity in definitions results in differences in cohorts of patients, and this impacts the procedure in model development and testing. It is also critical to understand how these definitions impact the model performance and more importantly, the models remain stable even when the definitions change. It is crucial to comprehend the field of machine learning encompasses an array of models amenable to a vast variety of tasks but that have their peculiarities and shortcomings. Some algorithms like deep learning are good at working with large data and complex relationships but are data-hungry, are usually black boxes, and can lack scalability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the same note, while complex models like artificial neural networks do provide accuracy, other models, for instance, logistic regression models, provide accuracy but with certain amounts of simplicity in their workings. Compiling a careful analysis of these models, it is possible to pinpoint which is the most suitable for sepsis detection considering the conditions that the model must address.\u003c/p\u003e\n\u003cp\u003eSeveral issues are tied to the identification of sepsis using the technique of machine learning. Another challenge related to datasets is the number of sepsis patients can be tens or hundreds of times less than in the non-sepsis group. Correspondingly, such an imbalance of data distribution may lead to chiefly focusing on the events of the majority class and missing important sepsis cases. The other challenge is that medical data has a lot of noise which can be defined as any unwanted signal that can be present in data. Many forecasting models are highly sensitive to errors in the data, the presence of missing values, and other similar discrepancies that reduce the predictive accuracy of the model. Solving these challenges requires the use of complex pre-processing of the models and related methodologies. Besides the factual constraints, one identifies the following questions of ethical nature. There are issues of privacy, security, and consent whenever a patient\u0026rsquo;s data is used in machine learning. These ethical issues have to be met to ensure that machine learning has been deployed in responsibly detecting sepsis.\u003c/p\u003e"},{"header":"8.\tConclusion","content":"\u003cp\u003eDeep learning (DL) and machine learning (ML) models have the incredible potential to improve early sepsis recognition. This is crucial for improving the patient\u0026apos;s survival rate and efficiently reducing the morbidity and mortality associated with life-threatening situations. By utilizing various data sources, like electronic health records (EHRS), vital signs, as well as laboratory results, the above models provide accurate and timely predictions that outclass conventional diagnostic techniques. This was to ensure that the final SLR included only studies of high quality and relevance, with 80 studies summit the essential norms for insertion in the final study. Despite their potential, several challenges hinder the deployment of these technologies in clinical settings. Data quality challenges, such as missing or inconsistent records, and the unbalancing between septic and non-septic patients present a significant barrier to model accuracy. Moreover, the complicated nature of deep learning algorithms, often interpreted as \u0026quot;black boxes,\u0026quot; complicates physician trust and adoption.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe integration of strong feature engineering, good data standardization, and more interpretable models are extremely important to overcome such obstacles and strengthen model consistency. Additionally, attempting to address the moral and legal consequences related to patient privacy protection, written consent, and equity is extremely critical. Ensuring compliance to regulate gdpr while reducing discrimination in predictive models is vital to fostering trust in these technology development solutions. Overall, ml and dl models present a groundbreaking potential for sepsis diagnosing, also balanced approach for addressing these challenges would be necessary for their wide adoption and efficiency in clinical experience.\u0026nbsp;\u003c/p\u003e"},{"header":"9.\tFuture Work","content":"\u003cp\u003eFuture research in; machine learning for sepsis identification must focus on dealing with the challenges of data integrity and class imbalance. As sepsis is a rare occurrence, strategies for addressing these problems, including advanced data augmentation methods and synthetic data processing, will be crucial to improve model performance. Additionally, the development of real-time prediction mechanisms that can constantly monitor patient records and trigger early interventions could significantly reduce the time to treatment, thus improving survival rates. Researchers should further explore using multi-modal data, including both structured and unstructured clinical information, for a more comprehensive understanding of patient conditions and improved prediction accuracy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother important point for future work is to improve the interpretability of advanced ML models, especially deep learning models, to make them clearer and more understandable to health professionals. Using tools like SHAP and LIME, researchers can gain additional insight into the importance of features, but further development will provide more actionable insights for clinical decision-making. Additionally, as healthcare settings vary widely, external validation and adaptation of these models to diverse medical environments are essential for ensuring generalizability. Future studies should also explore integrating these technologies into clinical workflow, ensuring they are all effective and easily implemented by healthcare providers. The focus must be on ensuring that such models not only enhance sepsis detection but also contribute to improving overall patient outcomes and healthcare system performance.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIntensive Care Units (ICUs)\u003c/p\u003e\n\u003cp\u003eMachine Learning (ML)\u003c/p\u003e\n\u003cp\u003eDeep Learning (DL)\u003c/p\u003e\n\u003cp\u003eElectronic Health Records (EHRs)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSystematic Literature Review (SLR)\u003c/p\u003e\n\u003cp\u003eMulti-task Gaussian Process Recurrent Neural Network (MGP-RNN)\u003c/p\u003e\n\u003cp\u003eSupport Vector Machine (SVMs)\u003c/p\u003e\n\u003cp\u003eGradient Boosting Machines (GBM)\u003c/p\u003e\n\u003cp\u003eRandom Forest (RF)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConvolutional Neural Networks (CNN)\u003c/p\u003e\n\u003cp\u003eSHAP (Shapley Additive Explanations)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study is a systematic literature review and does not involve any human participants or personal data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a review article and does not contain any primary data. All data analyzed during this study were obtained from previously published articles in publicly available databases such as IEEE Xplore, ACM Digital Library, and Scopus. A complete list of the references is included in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMZ: Conceptualization, methodology design, data curation, initial manuscript draft.\u003c/p\u003e\n\u003cp\u003eID: Supervision, critical revision, and correspondence handling.\u003c/p\u003e\n\u003cp\u003eNS: Literature review, analysis of machine learning techniques.\u003c/p\u003e\n\u003cp\u003eBE: Analysis of datasets and medical interpretation.\u003c/p\u003e\n\u003cp\u003eSM: Draft refinement, formatting, and proofreading.\u003c/p\u003e\n\u003cp\u003eZT: Supervision, technical review, validation, and co-correspondence.\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli dir=\"LTR\"\u003e Fleuren, L. 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Wu et al., \u0026ldquo;A customised down-sampling machine learning approach for sepsis prediction,\u0026rdquo; International Journal of Medical Informatics, vol. 184, p. 105365, Apr. 2024, doi: 10.1016/j.ijmedinf.2024.105365.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Deep learning, Machine learning, Detection, Sepsis Neural Network","lastPublishedDoi":"10.21203/rs.3.rs-6731418/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6731418/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSepsis, a life-threatening disease characterized by the body’s severe response to infection, remains a worldwide health issue causing mortality and morbidity. The complexity and rapid growth of sepsis make it challenging to detect in its early phases using traditional techniques. Thus, machine learning (ml) and deep learning (dl) methods have emerged as promising tools, offering the potential to process vast amounts of electronic health data, detect patterns, and predict the onset of sepsis earlier than conventional techniques. This systematic review critically examines the use of ML and DL models for sepsis detection and prediction with application across diverse clinical datasets i.e. Electronic Health Records (EHRs), vital signs monitoring systems, and large-scale databases. Through a comprehensive search of the relevant literature, this review synthesizes findings from over 125 studies, exploring the effectiveness of various computational methods more than 1500. The process of systematic literature review SLR included accessing articles from IEEE, ACM, and Scopus, with deletion of duplicate papers, articles from languages other than English, and outdated studies that resulted in only 80 valid studies. These methods range from simpler algorithms i.e. decision trees and support vector machines, to other models i.e., neural networks and ensemble techniques. Each model's capacity to handle the complexity of sepsis data is thoroughly analyzed. Besides this, the review also highlights key challenges inside the field, data quality problems, the generalization of models throughout patients with different populations, and ethical considerations related. These challenges pose barriers to the adoption of ML and DL for sepsis recognition in real-world clinical settings. In conclusion, the study highlights the need for advanced feature engineering, the use of ensemble techniques, advancement of integrated and real-time sepsis prediction systems. Such models improve accuracy, robustness, and scalability of predictive models for effective interventions. By addressing the current limitations with refining, these models in sepsis identification could transform current clinical practice with improved clinical outcomes.\u003c/p\u003e","manuscriptTitle":"Revolutionizing Sepsis Diagnosis Using Machine Learning and Deep Learning Models: A Systematic Literature Review ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 18:16:08","doi":"10.21203/rs.3.rs-6731418/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-30T06:42:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-29T09:05:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-28T09:29:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-26T17:38:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-21T19:47:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113289879550898821288115720204732826499","date":"2025-06-05T14:20:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165542384222430705288885231758593614768","date":"2025-06-05T13:42:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179715869905468321186114348677075704601","date":"2025-06-05T09:48:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202461516394090881924237800820472834807","date":"2025-06-05T04:34:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55022774018706596285231948474136046052","date":"2025-06-05T04:06:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78680553560936698869667062340627640770","date":"2025-06-05T03:02:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-05T02:59:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-03T23:23:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-03T23:23:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-05-23T09:20:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79ecb1ef-7807-4389-b65f-f9c26a3ab434","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:23:10+00:00","versionOfRecord":{"articleIdentity":"rs-6731418","link":"https://doi.org/10.1186/s12879-025-11423-2","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-10-23 16:16:41","publishedOnDateReadable":"October 23rd, 2025"},"versionCreatedAt":"2025-06-10 18:16:08","video":"","vorDoi":"10.1186/s12879-025-11423-2","vorDoiUrl":"https://doi.org/10.1186/s12879-025-11423-2","workflowStages":[]},"version":"v1","identity":"rs-6731418","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6731418","identity":"rs-6731418","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}