Leveraging High-Frequency Clinical Data for Early Septic Shock Detection with Advanced Machine Learning Frameworks

Leveraging High-Frequency Clinical Data for Early Septic Shock Detection with Advanced Machine Learning Frameworks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Leveraging High-Frequency Clinical Data for Early Septic Shock Detection with Advanced Machine Learning Frameworks Adrita Anwar, Muhammad E. H. Chowdhury, Abdulrahman Al-Ansari, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7455197/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Septic shock, the most critical and life-threatening form of sepsis, poses a significant challenge in early detection, often resulting in high mortality, increased morbidity, substantial healthcare costs, and poor clinical outcomes. Timely intervention within critical hours is essential to improving prognosis, thereby highlighting the importance of accurate early prediction. In this study, we employed the HiRID dataset, a high time resolution ICU dataset that offers more frequent observations compared to the two widely used publicly available intensive care unit (ICU) datasets, MIMIC-III and eICU. To our knowledge, this is the first study to apply HiRID for septic shock prediction. Unlike previous studies that predominantly emphasize aggregated metrics, this study introduces a predictive model using machine learning to separately evaluate performance outcomes for both positive and negative classes, thereby increasing methodological transparency. In resource-limited critical care settings such as ICUs, frequent alarms can significantly impede clinical efficiency and timely patient care. Consequently, this study prioritizes the development of precise, short-term predictive models for five prediction windows (0.2, 0.4, 0.6, 0.8, and 1.0 hours). These short intervals were deliberately selected to facilitate immediate and actionable interventions, rather than forecasting several hours in advance, which may offer less clinical utility. We trained ten machine learning models to forecast septic shock using stratified 5-fold cross-validation with strict patient-level data separation to ensure robustness and prevent data leakage. For the 0.2-hour onset prediction task, our best-performing fold achieved perfect scores across all key metrics—accuracy, AUC, precision, recall, and F1-score. Even in the least favourable fold, the model demonstrated robust performance with 100% accuracy, an AUC of 0.97, class-wise precision of (1.00, 1.00), recall of (1.00, 0.91), and F1-scores of (1.00, 0.95), indicating consistent and reliable predictive capability. These results underscore the effectiveness and clinical utility of our framework for early septic shock prediction and its potential to support timely, actionable decision-making in critical care settings. Sepsis Septic Shock Early Prediction Intensive Care Unit (ICU) Electronic Health Record (EHR) Machine Learning Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Jan, 2026 Reviews received at journal 28 Dec, 2025 Reviewers agreed at journal 23 Dec, 2025 Reviews received at journal 10 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers invited by journal 10 Oct, 2025 Editor invited by journal 24 Sep, 2025 Editor assigned by journal 02 Sep, 2025 Submission checks completed at journal 02 Sep, 2025 First submitted to journal 25 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7455197","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":532252021,"identity":"df8d95d9-f406-43bf-a785-d6a1e29ffc9d","order_by":0,"name":"Adrita Anwar","email":"","orcid":"","institution":"University of Dhaka","correspondingAuthor":false,"prefix":"","firstName":"Adrita","middleName":"","lastName":"Anwar","suffix":""},{"id":532252022,"identity":"d8d36e5c-ca73-4422-a93c-84a25f9ad929","order_by":1,"name":"Muhammad E. H. 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In this study, we employed the HiRID dataset, a high time resolution ICU dataset that offers more frequent observations compared to the two widely used publicly available intensive care unit (ICU) datasets, MIMIC-III and eICU. To our knowledge, this is the first study to apply HiRID for septic shock prediction. Unlike previous studies that predominantly emphasize aggregated metrics, this study introduces a predictive model using machine learning to separately evaluate performance outcomes for both positive and negative classes, thereby increasing methodological transparency. In resource-limited critical care settings such as ICUs, frequent alarms can significantly impede clinical efficiency and timely patient care. Consequently, this study prioritizes the development of precise, short-term predictive models for five prediction windows (0.2, 0.4, 0.6, 0.8, and 1.0 hours). These short intervals were deliberately selected to facilitate immediate and actionable interventions, rather than forecasting several hours in advance, which may offer less clinical utility. We trained ten machine learning models to forecast septic shock using stratified 5-fold cross-validation with strict patient-level data separation to ensure robustness and prevent data leakage. For the 0.2-hour onset prediction task, our best-performing fold achieved perfect scores across all key metrics—accuracy, AUC, precision, recall, and F1-score. Even in the least favourable fold, the model demonstrated robust performance with 100% accuracy, an AUC of 0.97, class-wise precision of (1.00, 1.00), recall of (1.00, 0.91), and F1-scores of (1.00, 0.95), indicating consistent and reliable predictive capability. 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