Construction and Application of Early Warning Model for Ischemic Colitis in Emergency Patients Based on Machine Learning

Construction and Application of Early Warning Model for Ischemic Colitis in Emergency Patients Based on Machine Learning | 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 Construction and Application of Early Warning Model for Ischemic Colitis in Emergency Patients Based on Machine Learning Minzhe Lang, Haoyue Hu, Minxuan Xu, Peiyuan Shou, Wenbin Chen, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7020933/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Ischemic colitis (IC), caused by reduced blood flow to the intestines, often presents with nonspecific early symptoms, leading to diagnostic delays and severe complications like necrosis or perforation. Current diagnostics (clinical evaluation, lab tests, imaging) lack sensitivity and specificity in early stages, highlighting the need for new predictive tools. This study proposes a machine learning model integrating clinical data, blood tests, and imaging descriptors to enable early IC detection at initial medical contact. Methods: Data from IC patients’ initial visits (Oct 2015–Jun 2022, Wenzhou Medical University) were analyzed. Mutual information selected key features; six models (e.g., random forest, logistic regression) were built. The top-performing model was streamlined and externally validated using first-contact data from Ningbo Second Hospital. Results: The random forest model, derived from first medical contact data of 427 IC patients and 507 control patients, demonstrated the highest performance, achieving an area under the curve (AUC) of 0.9251 and an accuracy of 0.8936 in the test data set. The model, optimized with 21 critical features, showed an AUC of 0.9191 and an accuracy of 0.8510. External validation yielded an AUC of 0.9963 and an accuracy of 0.9369. Conclusions: The RF-based IC model achieved superior diagnostic accuracy. Post-optimization, it maintained performance and demonstrated strong generalizability in external validation, underscoring its clinical utility. Ischemic colitis Machine learning Random Forest Early diagnosis Predictive model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Ischemic colitis (IC) is a condition characterized by acute or chronic ischemic injury, resulting from diminished or disrupted blood flow, which leads to inadequate blood supply to the colon [ 1 , 2 ] . In the United States, the incidence of IC is approximately 4.5 cases per 100,000 individuals, with a slightly higher prevalence observed in females compared to males. In recent years, the incidence has progressively risen, attributed to an aging population and the increasing prevalence of chronic diseases [ 3 , 4 ] . The clinical presentation of IC typically includes abdominal pain, hematochezia, diarrhea, and abdominal distention [ 5 ] . Additionally, some patients may experience other symptoms, such as nausea, vomiting, weight loss, and dehydration [ 6 ] . In more severe cases, complications such as intestinal obstruction, colonic perforation, and peritonitis may arise, with an overall reported mortality rate of 22% [ 7 ] . The diagnosis of IC currently depends primarily on clinical symptoms, laboratory tests, imaging assessments, and endoscopic examinations, with colonoscopy considered the gold standard for diagnosis. However, its utility in emergency rapid diagnosis is constrained by the prolonged preoperative preparation required [ 8 , 9 ] , which results in elevated rates of misdiagnosis and missed diagnoses among emergency IC cases. This issue is particularly evident in early-stage cases that lack typical symptoms, where delayed diagnosis can lead to severe outcomes. IC may progress rapidly to intestinal necrosis or perforation, thereby heightening the risk of complications, exacerbating the patient’s condition, and potentially becoming life-threatening. As a result, the development of an auxiliary diagnostic tool that can facilitate the early and accurate identification of IC is of paramount importance. In recent years, the progress of artificial intelligence has led to the demonstration of machine learning (ML) algorithms as powerful tools for diagnostic assistance in the medical field, enabling the processing of large datasets to generate precise predictions [ 10 , 11 ] . This study seeks to develop and validate a rapid diagnostic prediction model based on machine learning, which incorporates clinical data, blood test indicators, and CT imaging results from patients’ initial emergency department visits. The model aims to facilitate early identification of IC patients, reduce the rate of missed diagnoses, and ultimately enhance clinical outcomes. It is anticipated that this approach will markedly improve the early detection of IC, minimize the occurrence of severe complications, and provide solid support for clinical decision-making. Materials and methods Patient selection This retrospective analysis encompassed 427 individuals diagnosed with IC at the First Affiliated Hospital of Wenzhou Medical University from October 2015 to June 2022. All participants were initially diagnosed upon presenting with IC-related symptoms, such as abdominal discomfort and hematochezia. During the same period, 507 individuals, who sought medical attention for abdominal pain or hematochezia but were ultimately diagnosed with conditions other than IC, were included in the control group. Furthermore, to assess the generalizability of the developed model, an external validation set was constructed using data from 61 IC patients and 50 non-IC patients at the Second Affiliated Hospital of Ningbo University. This external cohort was utilized to evaluate the model’s applicability and performance across diverse institutions and patient populations. Ethical approval was obtained from the Ethics Committees of both hospitals (approval number: KY2024-R097). Given the retrospective nature of the study, the requirement for informed consent was waived by the Ethics Committees in accordance with national regulations and institutional protocols. Inclusion and exclusion criteria Inclusion criteria: (1) Complete clinical data; (2) Age > 18 years; (3) Primary clinical manifestations of abdominal pain with or without hematochezia; (4) Final diagnosis of IC based on colonoscopy or sigmoidoscopy examination within 48 h, according to the 2015 American Gastroenterological Association Clinical Guidelines for IC [ 12 ] ; (5) Abdominal CT examination performed. Exclusion criteria: (1) Incomplete clinical data; (2) Age ≤ 18 years; (3) Presence of hematemesis, hemoptysis, or bleeding from oral, nasal, or pharyngeal regions; (4) Self-discharge or deceased patients; (5) Patients without abdominal CT examination. The control group consisted of patients who met the above inclusion criteria but were diagnosed with abdominal symptoms due to other causes, with IC being ruled out at discharge. Data collection Data collection included: (1) Basic clinical information: demographic data (name, gender, age, body mass index, etc.), medical history (hypertension, renal insufficiency, medication history, smoking, alcohol consumption, surgical history, etc.); (2) Present illness: chief complaints including hematochezia, abdominal pain, etc.; (3) Laboratory tests: complete blood count, blood biochemistry, routine coagulation profile, etc.; (4) Imaging data: abdominal CT descriptions. Statistical analysis Statistical analyses were conducted using Python (3.8.0). For both the development and external validation cohorts, continuous variables were analyzed based on their distribution characteristics. Data that followed a normal distribution were expressed as mean ± standard deviation (SD), whereas non-normally distributed data were represented by the median and interquartile range. Categorical variables were reported as counts and percentages. Comparisons of all data were considered statistically significant at P < 0.05. Mutual information was utilized for feature selection to maintain consistency during variable screening. The dataset was then partitioned into training and validation sets in a 9:1 ratio. Six ML methods were employed: random forest (RF), Gaussian naive Bayes, fully connected neural networks, support vector machines, logistic regression, and Gaussian process classification models. The area under the receiver operating characteristic (AUROC) curve and accuracy metrics were used to assess the performance of each model. Upon identification of the optimal model, it was simplified by analyzing the variations in ROC curves and accuracy relative to feature variables, resulting in a more streamlined version. A comprehensive evaluation of the model was subsequently performed using various methods, including AUC, accuracy, decision curve analysis (DCA), and calibration curves, followed by external validation (Fig. 1 ). Results Patient characteristics A total of 427 individuals initially diagnosed with IC at the First Affiliated Hospital of Wenzhou Medical University between October 2015 and June 2022 were included in the case group for this study. The control group consisted of 507 individuals who presented with abdominal pain or hematochezia during the same period but were ultimately diagnosed with abdominal symptoms attributed to conditions other than IC. An external validation dataset was sourced from the Second Affiliated Hospital of Ningbo University, comprising initial clinical data from 61 IC-positive and 50 IC-negative patients. Information on basic demographics, present illness history, laboratory test results, and imaging data were gathered and analyzed for all patients. The characteristics of both IC-positive and IC-negative patients are presented in the following table (Table 1 ). Table 1 Demographic and clinical characteristics of the study population in the First Affiliated Hospital of Wenzhou Medical University. Variables Total (n = 934) non-IC(n = 507) IC(n = 427) p Age 61.34 (15.94) 56.10 (18.05) 67.57 (9.88) < 0.001 BPS 131.65 (21.10) 128.25 (20.58) 135.65 (21.02) < 0.001 heart rate 82.21 (16.70) 83.90 (16.79) 80.20 (16.38) < 0.001 WBC 10.01 (5.80) 9.25 (6.68) 10.91 (4.37) < 0.001 basophil% 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) < 0.001 Lymphocyte% 0.19 (0.11) 0.21 (0.12) 0.17 (0.09) < 0.001 Neutrophil 7.64 (4.53) 6.84 (4.60) 8.59 (4.25) < 0.001 Monocytes 0.57 (0.33) 0.52 (0.30) 0.63 (0.34) < 0.001 Basophils 0.02 (0.03) 0.02 (0.03) 0.02 (0.02) 0.032 MCV 89.41 (7.26) 88.73 (7.63) 90.22 (6.72) 0.001 MCH 30.39 (10.55) 29.82 (2.89) 31.05 (15.26) 0.102 RDW 13.65 (2.04) 13.78 (2.10) 13.50 (1.96) 0.033 PLT 227.89 (73.22) 234.27 (78.47) 220.31 (65.74) 0.003 PCT 0.22 (0.07) 0.23 (0.07) 0.22 (0.06) 0.003 PDW 14.30 (2.53) 14.50 (2.48) 14.05 (2.56) 0.009 ALT 26.50 (34.80) 29.18 (45.54) 23.33 (13.13) 0.006 Glucose 7.44 (3.96) 7.14 (4.40) 7.79 (3.33) 0.011 creatinine 83.52 (92.15) 91.62 (115.45) 73.97 (51.27) 0.002 Cl 103.34 (4.10) 103.09 (3.71) 103.64 (4.51) 0.044 PT 13.60 (1.67) 13.72 (2.03) 13.45 (1.09) 0.010 INR 1.05 (0.18) 1.06 (0.22) 1.03 (0.12) 0.005 fibrinogen 3.82 (1.25) 3.74 (1.37) 3.91 (1.09) 0.033 APTT 35.95 (5.20) 36.43 (5.16) 35.39 (5.20) 0.003 APTT-RATIO 1.00 (0.14) 1.01 (0.14) 0.98 (0.14) 0.003 D-dimer 1.93 (3.51) 1.31 (2.71) 2.62 (4.12) < 0.001 sex 502.00 (53.75%) 203.00 (40.04%) 299.00 (70.02%) < 0.001 Diabetes Mellitus 154.00 (16.49%) 63.00 (12.43%) 91.00 (21.31%) < 0.001 ‌Hypertension‌ 420.00 (44.97%) 175.00 (34.52%) 245.00 (57.38%) < 0.001 Hyperlipidemia 13.00 (1.39%) 3.00 (0.59%) 10.00 (2.34%) 0.023 Atrial Fibrillation 19.00 (2.11%) 6.00 (1.18%) 13.00 (3.32%) 0.027 Abdominal muscle tension 81.00 (8.67%) 59.00 (11.64%) 22.00 (5.15%) < 0.001 epigastric pain 147.00 (15.76%) 99.00 (19.57%) 48.00 (11.24%) < 0.001 lower abdominal pain 337.00 (36.12%) 144.00 (28.46%) 193.00 (45.20%) < 0.001 periumbilical pain 155.00 (16.61%) 51.00 (10.06%) 104.00 (24.41%) < 0.001 Left abdominal pain 87.00 (9.31%) 25.00 (4.93%) 62.00 (14.52%) < 0.001 Right abdominal pain 122.00 (13.06%) 103.00 (20.32%) 19.00 (4.45%) < 0.001 flatulency 148.00 (15.86%) 100.00 (19.72%) 48.00 (11.27%) < 0.001 melena 64.00 (6.86%) 35.00 (6.90%) 29.00 (6.81%) 0.954 constipated 51.00 (5.47%) 36.00 (7.10%) 15.00 (3.52%) 0.017 formless stool 640.00 (68.52%) 260.00 (51.28%) 380.00 (88.99%) < 0.001 Computed Tomography 828.00 (88.65%) 424.00 (83.63%) 404.00 (94.61%) < 0.001 Intestinal wall edema 156.00 (17.83%) 18.00 (3.84%) 138.00 (33.99%) < 0.001 Thickening of intestinal wall 319.00 (36.42%) 97.00 (20.68%) 222.00 (54.55%) < 0.001 Intestinal wall exudation 180.00 (20.55%) 25.00 (5.33%) 155.00 (38.08%) < 0.001 pneumatosis intestinalis 57.00 (6.51%) 52.00 (11.09%) 5.00 (1.23%) < 0.001 Ascites 59.00 (6.74%) 41.00 (8.74%) 18.00 (4.42%) 0.011 transverse colon 83.00 (9.47%) 14.00 (2.99%) 69.00 (16.95%) < 0.001 splenic flexure 50.00 (5.71%) 5.00 (1.07%) 45.00 (11.08%) < 0.001 descending colon 216.00 (24.66%) 24.00 (5.12%) 192.00 (47.17%) < 0.001 sigmoid colon 147.00 (16.78%) 31.00 (6.61%) 116.00 (28.50%) < 0.001 small intestine 60.00 (6.85%) 51.00 (10.87%) 9.00 (2.21%) < 0.001 SBP: Systolic Blood Pressure, WBC: White Blood Cell, MCV: Mean Corpuscular Volume, MCH: mean corpuscular hemoglobin, RDW: Red Blood Cell Distribution Width, PLT: Platelet, PCT: Plateletcrit, PDW: Platelet Distribution Width, ALT: Alanine Aminotransferase, PT: Prothrombin time, INR: International normalized ratio, APTT: Activated Partial Thromboplastin Time. Model construction and evaluation All patients were screened based on a unified set of inclusion criteria. The Python programming language was employed, and features were ranked using the mutual information method (Fig. 2 ). Following this, six ML models were trained and developed according to the order of feature importance: RF, Gaussian naive Bayes, fully connected neural networks, support vector machines, logistic regression, and Gaussian process classification. Among the six models, the RF model demonstrated the most exceptional overall performance, particularly in terms of classification accuracy and robustness (Table 3 ). This model achieved an AUROC value of 0.9251, which was close to the Gaussian process classification model’s value of 0.9354, with a 95% confidence interval (CI) of (0.9029, 0.9396), signifying its robust performance. Regarding classification accuracy, the RF model yielded the highest value of 0.8936, accompanied by a 95% CI of (0.8191, 0.8832), outperforming all other models. Although the Gaussian process classification model exhibited a slightly higher AUROC than the RF model, its accuracy was marginally lower. The RF model also demonstrated a higher recall rate (0.90) for negative samples, showing a stronger capability in identifying negative cases. Moreover, the RF model achieved superior F1-scores for both positive and negative classes, reflecting its advantage in balancing the handling of both types of samples. Taking into account classification accuracy, robustness, and the ability to detect negative cases, the RF model was chosen as the optimal model for further simplification and validation. Feature optimization and simplification During the initial stage of model development, all 99 features were incorporated. However, to streamline the model and enhance its applicability in clinical practice, feature optimization testing was performed. Based on the ranking of features using mutual information, the performance of the RF model was evaluated under varying numbers of features. When the feature set was reduced to 21, the model’s AUC and accuracy remained high, showing only a slight decrease compared to the results with 99 features (Fig. 3 ). This optimized model considerably decreased the complexity of the original model while enhancing its practical utility in clinical settings. Based on the results of feature quantity optimization, 21 features were selected for the construction of the RF model. The model’s performance was evaluated, revealing that the final RF model achieved an AUC of 0.9191 (Fig. 4 ) on the test set, demonstrating discriminative capability comparable to the previous model with 99 features (AUC 0.9251). This indicates that, despite the reduction in feature count, the model retained strong discriminative power. The accuracy was 0.8510, with a 95% CI of (0.7979, 0.8723), demonstrating stable discriminative performance on the test set with minimal error range, approaching the results achieved with all 99 features (0.8936). Subsequently, DCA was conducted to assess the model’s practical clinical applicability (Fig. 5 ). The DCA results revealed high net benefits across various thresholds, suggesting considerable value in clinical settings. To further evaluate the model’s impact across diverse patient populations, clinical impact curves (CIC) were plotted to validate both predictive accuracy and clinical effectiveness (Fig. 6 ). The results showed that, across different thresholds, the model effectively predicted both positive and negative cases, reinforcing its reliability. External validation of the model The final RF model was evaluated using an external validation dataset from Ningbo Second Hospital (Table 2 ). The AUC obtained on this validation set was 0.9963, demonstrating that the model preserved its strong discriminative power across different datasets, with an accuracy of 0.9369. This further affirmed the model’s generalization capacity (Fig. 7 ). The validation outcomes indicated that the model not only showed superior performance on the training data but also displayed significant robustness and stability when applied to external datasets, highlighting its potential for clinical application. Table 2 Demographic and clinical characteristics of the study population in external validation data Variables Total (n = 111) non-IC(n = 50) IC(n = 61) p Age 66.20 (11.97) 62.68 (14.07) 69.08 (9.05) 0.007 BPS 129.77 (18.24) 125.66 (17.43) 133.15 (18.34) 0.030 heart rate 81.37 (13.59) 78.88 (14.08) 83.41 (12.93) 0.083 WBC 8.34 (3.68) 7.28 (3.62) 9.20 (3.52) 0.006 basophil% 0.01 (0.03) 0.00 (0.00) 0.01 (0.04) 0.238 Lymphocyte% 0.18 (0.10) 0.19 (0.09) 0.18 (0.10) 0.512 Neutrophil 6.38 (3.46) 5.49 (3.48) 7.11 (3.30) 0.014 Monocytes 0.55 (0.31) 0.51 (0.35) 0.58 (0.27) 0.223 Basophils 0.02 (0.01) 0.02 (0.01) 0.02 (0.02) 0.168 MCV 92.30 (4.86) 92.39 (5.99) 92.24 (3.75) 0.876 MCH 30.47 (1.88) 30.45 (2.27) 30.48 (1.50) 0.948 RDW 13.68 (1.49) 13.93 (1.87) 13.47 (1.06) 0.129 PLT 208.85 (59.80) 206.62 (56.93) 210.67 (62.47) 0.722 PCT 0.19 (0.06) 0.20 (0.06) 0.19 (0.05) 0.734 PDW 14.93 (2.77) 14.48 (3.11) 15.29 (2.42) 0.135 ALT 33.00 (79.08) 49.39 (114.63) 19.57 (18.08) 0.075 Glucose 6.52 (3.09) 6.59 (3.82) 6.47 (2.37) 0.837 creatinine 74.14 (44.48) 81.15 (59.60) 68.39 (25.54) 0.163 Cl 105.30 (4.14) 105.03 (5.03) 105.51 (3.26) 0.559 PT 11.61 (1.67) 12.12 (2.20) 11.19 (0.87) 0.007 INR 1.15 (1.28) 1.07 (0.19) 1.21 (1.72) 0.529 fibrinogen 452.17 (196.89) 377.34 (233.71) 513.51 (134.07) < 0.001 APTT 30.52 (3.83) 31.44 (4.33) 29.77 (3.21) 0.026 APTT-RATIO 1.00 (0.14) 1.01 (0.14) 0.98 (0.14) 0.002 D-dimer 573.05 (656.12) 612.28 (818.81) 540.37 (485.91) 0.587 sex 48.00 (43.24%) 35.00 (70.00%) 13.00 (21.31%) < 0.001 Diabetes Mellitus 22.00 (19.82%) 8.00 (16.00%) 14.00 (22.95%) 0.361 ‌Hypertension‌ 55.00 (49.55%) 16.00 (32.00%) 39.00 (63.93%) < 0.001 Hyperlipidemia 11.00 (9.91%) 2.00 (4.00%) 9.00 (14.75%) 0.059 Atrial Fibrillation 1.00 (0.90%) 1.00 (2.00%) 0.00 (0.00%) 0.267 Abdominal muscle tension 16.00 (14.41%) 12.00 (24.00%) 4.00 (6.56%) 0.009 epigastric pain 35.00 (31.53%) 22.00 (44.00%) 13.00 (21.31%) 0.010 lower abdominal pain 33.00 (29.73%) 6.00 (12.00%) 27.00 (44.26%) < 0.001 periumbilical pain 24.00 (21.62%) 9.00 (18.00%) 15.00 (24.59%) 0.401 Left abdominal pain 15.00 (13.51%) 2.00 (4.00%) 13.00 (21.31%) 0.008 Right abdominal pain 13.00 (11.71%) 6.00 (12.00%) 7.00 (11.48%) 0.932 flatulency 32.00 (28.83%) 8.00 (16.00%) 24.00 (39.34%) 0.007 melena 14.00 (12.61%) 7.00 (14.00%) 7.00 (11.48%) 0.690 constipated 3.00 (2.70%) 0.00 (0.00%) 3.00 (4.92%) 0.112 formless stool 29.00 (26.13%) 1.00 (2.00%) 28.00 (45.90%) < 0.001 Computed Tomography 48.00 (43.24%) 48.00 (96.00%) 0.00 (0.00%) < 0.001 Intestinal wall edema 44.00 (39.64%) 1.00 (2.00%) 43.00 (70.49%) < 0.001 Thickening of intestinal wall 34.00 (30.63%) 2.00 (4.00%) 32.00 (52.46%) < 0.001 Intestinal wall exudation 26.00 (23.42%) 1.00 (2.00%) 25.00 (40.98%) < 0.001 pneumatosis intestinalis 4.00 (3.60%) 2.00 (4.00%) 2.00 (3.28%) 0.839 Ascites 3.00 (2.70%) 0.00 (0.00%) 3.00 (4.92%) 0.112 transverse colon 12.00 (10.81%) 1.00 (2.00%) 11.00 (18.03%) 0.007 splenic flexure 7.00 (6.31%) 0.00 (0.00%) 7.00 (11.48%) 0.013 descending colon 43.00 (38.74%) 0.00 (0.00%) 43.00 (70.49%) < 0.001 sigmoid colon 26.00 (23.42%) 0.00 (0.00%) 26.00 (42.62%) < 0.001 small intestine 7.00 (6.31%) 7.00 (14.00%) 0.00 (0.00%) 0.003 SBP: Systolic Blood Pressure, WBC: White Blood Cell, MCV: Mean Corpuscular Volume, MCH: mean corpuscular hemoglobin, RDW: Red Blood Cell Distribution Width, PLT: Platelet, PCT: Plateletcrit, PDW: Platelet Distribution Width, ALT: Alanine Aminotransferase, PT: Prothrombin time, INR: International normalized ratio, APTT: Activated Partial Thromboplastin Time. Table 3 Comparison of 6 Machine Learning Models Modle AUROC AUROC CI Accuracy Accuracy Cl Negative Recal Positive Recall Negative F1-Score Positive F1-Score Random forest 0.925 (0.903,0.940) 0.894 (0.819,0.883) 0.900 0.880 0.900 0.880 Gaussian Naive Bayes 0.660 (0.770,0.872) 0.660 (0.532,0.830) 0.500 0.860 0.620 0.690 Fully Connected Neural Network 0.923 (0.902,0.932) 0.861 (0.824,0.877) 0.910 0.800 0.880 0.840 Support Vector Machine 0.925 (0.899,0.937) 0.883 (0.819,0.894) 0.940 0.810 0.900 0.860 Logistic Regression 0.912 (0.842,0.925) 0.798 (0.755,0.862) 0.830 0.760 0.820 0.770 Gaussian Process Classifier 0.935 (0.887,0.936) 0.872 (0.798,0.883) 0.900 0.830 0.890 0.850 Discussion A predictive model for the early detection of IC has been successfully developed. Upon comparing various ML models, the RF model emerged as the most effective. After feature optimization, this model exhibited high predictive accuracy and strong generalization capabilities. Its applicability in real clinical environments was further assessed through external validation. The model demonstrates significant potential for aiding clinicians in the early identification of high-risk patients, facilitating timely interventions for the diagnosis of IC. In this study, the RF model exhibited superior performance, mainly attributed to its robust capabilities in processing nonlinear data and its high stability. When compared to other models, RF demonstrated exceptional proficiency in managing high-dimensional data, a critical factor when working with medical datasets that contain numerous features and variables. As shown in this study, RF achieved an AUC of 0.9251 and an accuracy of 0.8936 on the test set, reflecting its strong performance in classification tasks. Furthermore, RF offers significant advantages in dealing with missing data and mitigating overfitting [ 13 ] . In contrast, while logistic regression offers better interpretability, it is less effective at processing nonlinear data. Despite the Gaussian process classification model marginally outperforming RF in AUC (0.9354 vs. 0.9251), it is associated with greater complexity in practical use, particularly due to its high computational demands when handling large-scale datasets [ 14 ] . Thus, RF strikes an optimal balance between performance and simplicity. Feature optimization stands out as another significant aspect of this study. Initially, 99 features were used for model development and comparison, with the RF model emerging as the optimal selection. Using mutual information for feature ranking, performance testing was conducted across various feature thresholds, ultimately reducing the feature set to 21. This process notably simplified the model while maintaining its interpretability and applicability in clinical practice, with only a marginal loss in predictive accuracy. The streamlined model yielded a slightly lower AUC compared to the full-feature version (0.9191 vs. 0.9251), yet preserved a high level of accuracy (0.8510), thus striking an ideal balance between performance and feasibility [ 15 , 16 ] . From a clinical standpoint, the reduced feature set enhances the model’s practicality for implementation, allowing healthcare providers to generate reliable predictions using only 21 essential features. This simplification not only eliminates the need for numerous difficult-to-obtain or redundant variables but also reduces the data collection burden and minimizes the risk of errors in clinical settings. Furthermore, DCA and CIC were employed to assess the model’s clinical decision-making utility. The results from DCA revealed significant net benefits across various thresholds, demonstrating the model’s potential for supporting clinical decisions at different levels of risk. This analysis effectively validated the model’s ability to reduce unnecessary interventions and prevent missed diagnoses [ 17 , 18 ] . Additionally, external validation results reinforced the model’s clinical relevance. The AUC of 0.9963 obtained from the external dataset confirmed the model’s generalizability, indicating strong performance not only within the original dataset but also across different hospital populations. This is particularly important, as model efficacy often varies markedly across medical institutions and patient demographics. External validation thus provided robust evidence supporting the model’s reliability and scalability [ 19 ] . Although the model demonstrated strong performance in this study, several limitations warrant acknowledgment. First, while data from two hospitals were included, both hospitals were situated within the same province. To further substantiate the model’s applicability across broader contexts, large-scale, multi-center studies are necessary. Second, despite the optimization of features to reduce model complexity, the number of feature variables remains relatively high. Future investigations could focus on further simplification of the model or explore the inclusion of additional specific biomarkers to enhance its practical utility [ 20 , 21 ] . Lastly, although the RF model outperformed other models in terms of performance, its inherent "black box" nature poses a significant challenge. While the importance of various features was analyzed in this study, the model’s decision-making process remains challenging to interpret fully, which could potentially impact clinicians’ confidence in its clinical implementation [ 22 ] . Future advancements might involve the integration of more transparent, interpretable models or the development of explanation tools to enhance the clinical acceptance of RF models. Conclusion An early warning model for IC was successfully developed in this study utilizing the RF algorithm. Through feature optimization and subsequent model validation, the model’s predictive capabilities were confirmed, demonstrating its potential for clinical application and practicality. The model exhibits promising prospects for early IC diagnosis and necessitates further validation in large-scale clinical settings. Abbreviations ACU ,area under the curve AUROC , area under the receiver operating characteristic CI , confidence interval CIC , clinical impact curves DCA , decision curve analysis IC , ischemic colitis ML , machine learning RF , random forest ROC , receiver operating characteristic SD , standard deviation Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee in Clinical Research (ECCR) of the First Affiliated Hospital of Wenzhou Medical University (approval number: KY2024-R097) and conducted in accordance with the principles of the Declaration of Helsinki. Given the retrospective nature of the study, written informed consent was waived by the ethics committee. All procedures and analyses were performed in compliance with relevant guidelines and regulations. Consent for publication Not applicable. Availability of data and materials All data generated or analysed during this study are included in this published article. Competing interests The authors declare that there are no conflicts of interest to disclose in this study. Funding The present study was supported by the Science and Technology Planning Project of Wenzhou City (Y2020127) and the National Key R&D Program of China (No. 2021YFC3002205). Authors’ contributions Minzhe Lang: Writing the original draft, Investigation, Formal analysis, Methodology, Data curation, Conceptualization. Haoyue Hu: Formal analysis, Methodology, Investigation, Validation, Data curation. Minxuan Xu: Investigation, Data curation. Peiyuan Shou: Investigation, Data curation. Wenbin Chen: Validation, Data curation. Shaoce Zhi: Validation, Data curation. Guangliang Hong: Writing- review & editing, Funding acquisition, Conceptualization. Wenwen Li: Writing- review & editing, Validation, Formal analysis, Methodology, Supervision, Conceptualization. Xiaoqin Dai: Writing- review & editing, Validation, Formal analysis, Methodology, Supervision, Project administration, Conceptualization. Acknowledgements Not applicable. References Gandhi SK, Hanson MM, Vernava AM, et al. Ischemic colitis[J]. Volume 39. Diseases of the colon & rectum; 1996. pp. 88–100. Moszkowicz D, Mariani A, Trésallet C, et al. Ischemic colitis: the ABCs of diagnosis and surgical management[J]. J Visc Surg. 2013;150(1):19–28. Yadav S, Dave M, Varayil JE, et al. A population-based study of incidence, risk factors, clinical spectrum, and outcomes of ischemic colitis[J]. Clin Gastroenterol Hepatol. 2015;13(4):731–8. e6. Hines DM, McGuiness CB, Schlienger RG, et al. Incidence of ischemic colitis in treated, commercially insured hypertensive adults: a cohort study of US health claims data[J]. Am J Cardiovasc Drugs. 2015;15:135–49. FitzGerald JF, Hernandez III. L O. Ischemic colitis[J]. Clin Colon Rectal Surg. 2015;28(02):093–8. España PP, Capelastegui A, Gorordo I, et al. Development and validation of a clinical prediction rule for severe communityacquired pneumonia. Am J Respir Crit Care Med. 2006;174:1249–56. Tadros M, Majumder S, Birk JW. A review of ischemic colitis: is our clinical recognition and management adequate?[J]. Expert Rev Gastroenterol Hepatol. 2013;7(7):605–13. Misiakos EP, Tsapralis D, Karatzas T, et al. Advents in the diagnosis and management of ischemic colitis[J]. Front Surg. 2017;4:47. Iacobellis F, Berritto D, Fleischmann D, et al. CT findings in acute, subacute, and chronic ischemic colitis: suggestions for diagnosis[J]. Biomed Res Int. 2014;2014(1):895248. Connor CW. Artificial intelligence and machine learning in anesthesiology. Anesthesiology. 2019;131:1346–59. van Rein EAJ, van der Sluijs R, Voskens FJ, Lansink KWW, Houwert RM, Lichtveld RA, et al. Development and validation of a prediction model for prehospital triage of trauma patients. JAMA Surg. 2019;154:421–9. American College of Gastroenterology. 2018. ACG Clinical Guideline: Colon Ischemia. Accessed September 17, 2024. https://acgcdn.gi.org/wp-content/uploads/2018/04/ACG-Colon-Ischemia-Guideline-Summary.pdf Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. Rasmussen CE, Williams CK. Gaussian processes for machine learning. MIT Press; 2006. Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003;3(Mar):1157–82. Kuhn M, Johnson K. Applied predictive modeling. Springer; 2013. Obermeyer Z, Emanuel EJ. Predicting the future-big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216–9. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6):565–74. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMC Med. 2015;13(1):1–0. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y. Artificial intelligence in healthcare: past, present and future. Stroke vascular Neurol. 2017;2(4):230–43. Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018;15(4):233–4. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1(5):206–15. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-7020933","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495817196,"identity":"7a58a1bc-bba5-4102-87f6-0b16295cfd07","order_by":0,"name":"Minzhe Lang","email":"","orcid":"","institution":"the First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Minzhe","middleName":"","lastName":"Lang","suffix":""},{"id":495817197,"identity":"18199878-2321-4a41-8069-572d3d48d78e","order_by":1,"name":"Haoyue Hu","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haoyue","middleName":"","lastName":"Hu","suffix":""},{"id":495817198,"identity":"4fe7e5a6-f11d-48cb-92da-e8e70aacc65c","order_by":2,"name":"Minxuan Xu","email":"","orcid":"","institution":"the First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Minxuan","middleName":"","lastName":"Xu","suffix":""},{"id":495817199,"identity":"c747cd6e-2285-42ef-83ce-cfb8346da24c","order_by":3,"name":"Peiyuan Shou","email":"","orcid":"","institution":"the First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peiyuan","middleName":"","lastName":"Shou","suffix":""},{"id":495817200,"identity":"6ac701ca-4fc0-416c-bd88-72e5b47006b4","order_by":4,"name":"Wenbin Chen","email":"","orcid":"","institution":"the First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenbin","middleName":"","lastName":"Chen","suffix":""},{"id":495817201,"identity":"5f506ed7-3be2-4dfa-86ba-c8ad3c5acb64","order_by":5,"name":"Shaoce Zhi","email":"","orcid":"","institution":"the First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shaoce","middleName":"","lastName":"Zhi","suffix":""},{"id":495817202,"identity":"fe08afe4-2706-47b2-872c-1e8ae3edadb5","order_by":6,"name":"Guangliang Hong","email":"","orcid":"","institution":"the First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guangliang","middleName":"","lastName":"Hong","suffix":""},{"id":495817203,"identity":"f9c95465-d924-4380-a3d9-fbf8a468e520","order_by":7,"name":"Wenwen Li","email":"","orcid":"","institution":"the First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenwen","middleName":"","lastName":"Li","suffix":""},{"id":495817204,"identity":"0ffb83eb-8cfc-45a5-a408-2570e48e5bef","order_by":8,"name":"Xiaoqin Dai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYDCCAwzMDxL/2PDw8zcQr4XN4GFDmozkjAPEa2GQfNhw2MagIYFIHXw3kjcYJO44z2PAcIDxw8ccIrRInjlW8CDxzG0ec+YGZsmZ24jQYnC8x8Agge02j2XDATZmXqK0HOYxkEhgO8djcCCBWC1AWyQS2w6QoAXolzKDhDPJPJIzDjYT5xdgiG1++KPCzp6fv/ngh4/EaAG5DUozNhCnHknLKBgFo2AUjAIcAAAZMTuocMLtFgAAAABJRU5ErkJggg==","orcid":"","institution":"the First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoqin","middleName":"","lastName":"Dai","suffix":""}],"badges":[],"createdAt":"2025-07-01 13:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7020933/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7020933/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88645441,"identity":"949c3a4f-ddec-44d5-ab34-c9064815c522","added_by":"auto","created_at":"2025-08-08 16:25:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of the study design.\u003c/strong\u003e The First Affiliated Hospital of Wenzhou Medical University initially enrolled 1,000 patients, of whom 66 were excluded due to incomplete clinical data. The final model included 507 patients with ischemic colitis (IC) and 427 non-IC patients. Ningbo No.2 Hospital served as the external validation cohort, enrolling 130 patients, with 19 excluded due to incomplete data. The final validation group comprised 61 IC-positive and 50 IC-negative patients. IC: ischemic colitis.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7020933/v1/4193b518bd8b6f47ba10e9ad.png"},{"id":88643746,"identity":"f06913b1-9dbb-49c0-b937-db0f05c9c412","added_by":"auto","created_at":"2025-08-08 16:17:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":119915,"visible":true,"origin":"","legend":"\u003cp\u003eThe mutual information screening results are shown in the figure. Ultimately, the 21 optimal features with the highest mutual information values were selected.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7020933/v1/aa7f3c0b4361313a31d40e9c.png"},{"id":88645442,"identity":"317abc5d-7298-4f0c-981b-feeacab36a25","added_by":"auto","created_at":"2025-08-08 16:25:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92174,"visible":true,"origin":"","legend":"\u003cp\u003eTo simplify the model and enhance its clinical utility, we evaluated the performance of the random forest model with varying numbers of features. When the number of features was set to 21, the model maintained high levels of both AUC and accuracy.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7020933/v1/e94a10fc9ce848dde0b6cbec.png"},{"id":88645468,"identity":"11eae670-3b68-4ede-b712-734e1258671c","added_by":"auto","created_at":"2025-08-08 16:25:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35381,"visible":true,"origin":"","legend":"\u003cp\u003eThe simplified model achieved an AUC of 0.9191, demonstrating comparable discriminative ability to the original model built with 99 features.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7020933/v1/4175a1f8f6bdb9e36119fa0b.png"},{"id":88643770,"identity":"3a0daeff-e9f0-422c-89f3-35d9dbc8ce1c","added_by":"auto","created_at":"2025-08-08 16:17:37","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":53242,"visible":true,"origin":"","legend":"\u003cp\u003eThe DCA (Decision Curve Analysis) curve demonstrated that this model provides higher net benefits across varying threshold probabilities, indicating its strong clinical utility for practical applications.\u003c/p\u003e","description":"","filename":"image5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7020933/v1/87e4bd171d81b49068dbec1d.jpg"},{"id":88643766,"identity":"36e494c1-e2ca-4640-9854-fbf5440d1ab7","added_by":"auto","created_at":"2025-08-08 16:17:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":63572,"visible":true,"origin":"","legend":"\u003cp\u003eCIC (Clinical Impact Curve) analysis demonstrated that the model exhibits high robustness and strong generalization capability in the external validation dataset.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7020933/v1/30f20756770636ef7aa62e05.png"},{"id":88643757,"identity":"dc0b9c5c-0608-47bb-aaff-31b10cbb2a90","added_by":"auto","created_at":"2025-08-08 16:17:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":26388,"visible":true,"origin":"","legend":"\u003cp\u003eThe optimized random forest model was externally validated using an independent dataset from Ningbo No. 2 Hospital (comprising 61 IC-positive and 50 IC-negative patients). The validation results demonstrated excellent performance, with an AUC of 0.9960 and an accuracy of 0.9370.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7020933/v1/c6ad37688d2d2d67bbc71eea.png"},{"id":91326619,"identity":"892e1529-4f8b-4f2d-8fa4-c1a04b3a60b5","added_by":"auto","created_at":"2025-09-15 10:02:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1475232,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7020933/v1/99183ba9-c492-4cda-b1ae-457977d2c6e6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction and Application of Early Warning Model for Ischemic Colitis in Emergency Patients Based on Machine Learning","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIschemic colitis (IC) is a condition characterized by acute or chronic ischemic injury, resulting from diminished or disrupted blood flow, which leads to inadequate blood supply to the colon\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In the United States, the incidence of IC is approximately 4.5 cases per 100,000 individuals, with a slightly higher prevalence observed in females compared to males. In recent years, the incidence has progressively risen, attributed to an aging population and the increasing prevalence of chronic diseases\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The clinical presentation of IC typically includes abdominal pain, hematochezia, diarrhea, and abdominal distention\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Additionally, some patients may experience other symptoms, such as nausea, vomiting, weight loss, and dehydration\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In more severe cases, complications such as intestinal obstruction, colonic perforation, and peritonitis may arise, with an overall reported mortality rate of 22%\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe diagnosis of IC currently depends primarily on clinical symptoms, laboratory tests, imaging assessments, and endoscopic examinations, with colonoscopy considered the gold standard for diagnosis. However, its utility in emergency rapid diagnosis is constrained by the prolonged preoperative preparation required\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, which results in elevated rates of misdiagnosis and missed diagnoses among emergency IC cases. This issue is particularly evident in early-stage cases that lack typical symptoms, where delayed diagnosis can lead to severe outcomes. IC may progress rapidly to intestinal necrosis or perforation, thereby heightening the risk of complications, exacerbating the patient\u0026rsquo;s condition, and potentially becoming life-threatening. As a result, the development of an auxiliary diagnostic tool that can facilitate the early and accurate identification of IC is of paramount importance.\u003c/p\u003e\u003cp\u003eIn recent years, the progress of artificial intelligence has led to the demonstration of machine learning (ML) algorithms as powerful tools for diagnostic assistance in the medical field, enabling the processing of large datasets to generate precise predictions\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. This study seeks to develop and validate a rapid diagnostic prediction model based on machine learning, which incorporates clinical data, blood test indicators, and CT imaging results from patients\u0026rsquo; initial emergency department visits. The model aims to facilitate early identification of IC patients, reduce the rate of missed diagnoses, and ultimately enhance clinical outcomes. It is anticipated that this approach will markedly improve the early detection of IC, minimize the occurrence of severe complications, and provide solid support for clinical decision-making.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003ePatient selection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis retrospective analysis encompassed 427 individuals diagnosed with IC at the First Affiliated Hospital of Wenzhou Medical University from October 2015 to June 2022. All participants were initially diagnosed upon presenting with IC-related symptoms, such as abdominal discomfort and hematochezia. During the same period, 507 individuals, who sought medical attention for abdominal pain or hematochezia but were ultimately diagnosed with conditions other than IC, were included in the control group. Furthermore, to assess the generalizability of the developed model, an external validation set was constructed using data from 61 IC patients and 50 non-IC patients at the Second Affiliated Hospital of Ningbo University. This external cohort was utilized to evaluate the model\u0026rsquo;s applicability and performance across diverse institutions and patient populations. Ethical approval was obtained from the Ethics Committees of both hospitals (approval number: KY2024-R097). Given the retrospective nature of the study, the requirement for informed consent was waived by the Ethics Committees in accordance with national regulations and institutional protocols.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInclusion and exclusion criteria\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInclusion criteria:\u003c/p\u003e\u003cp\u003e(1) Complete clinical data;\u003c/p\u003e\u003cp\u003e(2) Age\u0026thinsp;\u0026gt;\u0026thinsp;18 years;\u003c/p\u003e\u003cp\u003e(3) Primary clinical manifestations of abdominal pain with or without hematochezia;\u003c/p\u003e\u003cp\u003e(4) Final diagnosis of IC based on colonoscopy or sigmoidoscopy examination within 48 h, according to the 2015 American Gastroenterological Association Clinical Guidelines for IC\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e;\u003c/p\u003e\u003cp\u003e(5) Abdominal CT examination performed.\u003c/p\u003e\u003cp\u003eExclusion criteria:\u003c/p\u003e\u003cp\u003e(1) Incomplete clinical data;\u003c/p\u003e\u003cp\u003e(2) Age\u0026thinsp;\u0026le;\u0026thinsp;18 years;\u003c/p\u003e\u003cp\u003e(3) Presence of hematemesis, hemoptysis, or bleeding from oral, nasal, or pharyngeal regions;\u003c/p\u003e\u003cp\u003e(4) Self-discharge or deceased patients;\u003c/p\u003e\u003cp\u003e(5) Patients without abdominal CT examination.\u003c/p\u003e\u003cp\u003eThe control group consisted of patients who met the above inclusion criteria but were diagnosed with abdominal symptoms due to other causes, with IC being ruled out at discharge.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData collection included:\u003c/p\u003e\u003cp\u003e(1) Basic clinical information: demographic data (name, gender, age, body mass index, etc.), medical history (hypertension, renal insufficiency, medication history, smoking, alcohol consumption, surgical history, etc.);\u003c/p\u003e\u003cp\u003e(2) Present illness: chief complaints including hematochezia, abdominal pain, etc.;\u003c/p\u003e\u003cp\u003e(3) Laboratory tests: complete blood count, blood biochemistry, routine coagulation profile, etc.;\u003c/p\u003e\u003cp\u003e(4) Imaging data: abdominal CT descriptions.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted using Python (3.8.0). For both the development and external validation cohorts, continuous variables were analyzed based on their distribution characteristics. Data that followed a normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), whereas non-normally distributed data were represented by the median and interquartile range. Categorical variables were reported as counts and percentages. Comparisons of all data were considered statistically significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eMutual information was utilized for feature selection to maintain consistency during variable screening. The dataset was then partitioned into training and validation sets in a 9:1 ratio. Six ML methods were employed: random forest (RF), Gaussian naive Bayes, fully connected neural networks, support vector machines, logistic regression, and Gaussian process classification models.\u003c/p\u003e\u003cp\u003eThe area under the receiver operating characteristic (AUROC) curve and accuracy metrics were used to assess the performance of each model. Upon identification of the optimal model, it was simplified by analyzing the variations in ROC curves and accuracy relative to feature variables, resulting in a more streamlined version. A comprehensive evaluation of the model was subsequently performed using various methods, including AUC, accuracy, decision curve analysis (DCA), and calibration curves, followed by external validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003ePatient characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 427 individuals initially diagnosed with IC at the First Affiliated Hospital of Wenzhou Medical University between October 2015 and June 2022 were included in the case group for this study. The control group consisted of 507 individuals who presented with abdominal pain or hematochezia during the same period but were ultimately diagnosed with abdominal symptoms attributed to conditions other than IC. An external validation dataset was sourced from the Second Affiliated Hospital of Ningbo University, comprising initial clinical data from 61 IC-positive and 50 IC-negative patients. Information on basic demographics, present illness history, laboratory test results, and imaging data were gathered and analyzed for all patients. The characteristics of both IC-positive and IC-negative patients are presented in the following table (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and clinical characteristics of the study population in the First Affiliated Hospital of Wenzhou Medical University.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;934)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003enon-IC(n\u0026thinsp;=\u0026thinsp;507)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIC(n\u0026thinsp;=\u0026thinsp;427)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61.34 (15.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56.10 (18.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e67.57 (9.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e131.65 (21.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e128.25 (20.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e135.65 (21.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eheart rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82.21 (16.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e83.90 (16.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80.20 (16.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.01 (5.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.25 (6.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.91 (4.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ebasophil%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.19 (0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.21 (0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.17 (0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.64 (4.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.84 (4.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.59 (4.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocytes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.57 (0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.52 (0.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.63 (0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasophils\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.02 (0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02 (0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02 (0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89.41 (7.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.73 (7.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90.22 (6.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.39 (10.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.82 (2.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.05 (15.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.65 (2.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.78 (2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.50 (1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e227.89 (73.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e234.27 (78.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e220.31 (65.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.22 (0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.23 (0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.22 (0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.30 (2.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.50 (2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.05 (2.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.50 (34.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.18 (45.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.33 (13.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.44 (3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.14 (4.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.79 (3.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecreatinine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83.52 (92.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91.62 (115.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73.97 (51.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e103.34 (4.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e103.09 (3.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e103.64 (4.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.60 (1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.72 (2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.45 (1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.05 (0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.06 (0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.03 (0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efibrinogen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.82 (1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.74 (1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.91 (1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.95 (5.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.43 (5.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.39 (5.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPTT-RATIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.01 (0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.98 (0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-dimer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.93 (3.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.31 (2.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.62 (4.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e502.00 (53.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e203.00 (40.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e299.00 (70.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes Mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e154.00 (16.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.00 (12.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91.00 (21.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026zwnj;Hypertension\u0026zwnj;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e420.00 (44.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e175.00 (34.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e245.00 (57.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.00 (1.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.00 (0.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.00 (2.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtrial Fibrillation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.00 (2.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.00 (1.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.00 (3.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbdominal muscle tension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e81.00 (8.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.00 (11.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.00 (5.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eepigastric pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e147.00 (15.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99.00 (19.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.00 (11.24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elower abdominal pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e337.00 (36.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e144.00 (28.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e193.00 (45.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eperiumbilical pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e155.00 (16.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.00 (10.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e104.00 (24.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft abdominal pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e87.00 (9.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.00 (4.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.00 (14.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRight abdominal pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e122.00 (13.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e103.00 (20.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.00 (4.45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eflatulency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e148.00 (15.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.00 (19.72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.00 (11.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emelena\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64.00 (6.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.00 (6.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.00 (6.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.954\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003econstipated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51.00 (5.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.00 (7.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.00 (3.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eformless stool\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e640.00 (68.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e260.00 (51.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e380.00 (88.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComputed Tomography\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e828.00 (88.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e424.00 (83.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e404.00 (94.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntestinal wall edema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e156.00 (17.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.00 (3.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e138.00 (33.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThickening of intestinal wall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e319.00 (36.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97.00 (20.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e222.00 (54.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntestinal wall exudation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e180.00 (20.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.00 (5.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e155.00 (38.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epneumatosis intestinalis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57.00 (6.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.00 (11.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.00 (1.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAscites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59.00 (6.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.00 (8.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.00 (4.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etransverse colon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83.00 (9.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.00 (2.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.00 (16.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esplenic flexure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50.00 (5.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.00 (1.07%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.00 (11.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edescending colon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e216.00 (24.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.00 (5.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e192.00 (47.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esigmoid colon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e147.00 (16.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.00 (6.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e116.00 (28.50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esmall intestine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60.00 (6.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.00 (10.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.00 (2.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eSBP: Systolic Blood Pressure, WBC: White Blood Cell, MCV: Mean Corpuscular Volume, MCH: mean corpuscular hemoglobin, RDW: Red Blood Cell Distribution Width, PLT: Platelet, PCT: Plateletcrit, PDW: Platelet Distribution Width, ALT: Alanine Aminotransferase, PT: Prothrombin time, INR: International normalized ratio, APTT: Activated Partial Thromboplastin Time.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel construction and evaluation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll patients were screened based on a unified set of inclusion criteria. The Python programming language was employed, and features were ranked using the mutual information method (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Following this, six ML models were trained and developed according to the order of feature importance: RF, Gaussian naive Bayes, fully connected neural networks, support vector machines, logistic regression, and Gaussian process classification.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong the six models, the RF model demonstrated the most exceptional overall performance, particularly in terms of classification accuracy and robustness (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This model achieved an AUROC value of 0.9251, which was close to the Gaussian process classification model\u0026rsquo;s value of 0.9354, with a 95% confidence interval (CI) of (0.9029, 0.9396), signifying its robust performance. Regarding classification accuracy, the RF model yielded the highest value of 0.8936, accompanied by a 95% CI of (0.8191, 0.8832), outperforming all other models.\u003c/p\u003e\u003cp\u003eAlthough the Gaussian process classification model exhibited a slightly higher AUROC than the RF model, its accuracy was marginally lower. The RF model also demonstrated a higher recall rate (0.90) for negative samples, showing a stronger capability in identifying negative cases. Moreover, the RF model achieved superior F1-scores for both positive and negative classes, reflecting its advantage in balancing the handling of both types of samples.\u003c/p\u003e\u003cp\u003eTaking into account classification accuracy, robustness, and the ability to detect negative cases, the RF model was chosen as the optimal model for further simplification and validation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFeature optimization and simplification\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDuring the initial stage of model development, all 99 features were incorporated. However, to streamline the model and enhance its applicability in clinical practice, feature optimization testing was performed. Based on the ranking of features using mutual information, the performance of the RF model was evaluated under varying numbers of features. When the feature set was reduced to 21, the model\u0026rsquo;s AUC and accuracy remained high, showing only a slight decrease compared to the results with 99 features (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This optimized model considerably decreased the complexity of the original model while enhancing its practical utility in clinical settings.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on the results of feature quantity optimization, 21 features were selected for the construction of the RF model. The model\u0026rsquo;s performance was evaluated, revealing that the final RF model achieved an AUC of 0.9191 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) on the test set, demonstrating discriminative capability comparable to the previous model with 99 features (AUC 0.9251). This indicates that, despite the reduction in feature count, the model retained strong discriminative power. The accuracy was 0.8510, with a 95% CI of (0.7979, 0.8723), demonstrating stable discriminative performance on the test set with minimal error range, approaching the results achieved with all 99 features (0.8936). Subsequently, DCA was conducted to assess the model\u0026rsquo;s practical clinical applicability (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The DCA results revealed high net benefits across various thresholds, suggesting considerable value in clinical settings. To further evaluate the model\u0026rsquo;s impact across diverse patient populations, clinical impact curves (CIC) were plotted to validate both predictive accuracy and clinical effectiveness (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The results showed that, across different thresholds, the model effectively predicted both positive and negative cases, reinforcing its reliability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eExternal validation of the model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe final RF model was evaluated using an external validation dataset from Ningbo Second Hospital (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The AUC obtained on this validation set was 0.9963, demonstrating that the model preserved its strong discriminative power across different datasets, with an accuracy of 0.9369. This further affirmed the model\u0026rsquo;s generalization capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The validation outcomes indicated that the model not only showed superior performance on the training data but also displayed significant robustness and stability when applied to external datasets, highlighting its potential for clinical application.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and clinical characteristics of the study population in external validation data\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003enon-IC(n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIC(n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66.20 (11.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.68 (14.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.08 (9.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e129.77 (18.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e125.66 (17.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e133.15 (18.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eheart rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e81.37 (13.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.88 (14.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83.41 (12.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.34 (3.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.28 (3.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.20 (3.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ebasophil%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.01 (0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01 (0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.18 (0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.19 (0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18 (0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.38 (3.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.49 (3.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.11 (3.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocytes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.55 (0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.51 (0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.58 (0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasophils\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.02 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02 (0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92.30 (4.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92.39 (5.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e92.24 (3.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.876\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.47 (1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.45 (2.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.48 (1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.948\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.68 (1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.93 (1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.47 (1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e208.85 (59.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e206.62 (56.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e210.67 (62.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.722\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.19 (0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.20 (0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.19 (0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.734\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.93 (2.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.48 (3.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.29 (2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.00 (79.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49.39 (114.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.57 (18.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.52 (3.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.59 (3.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.47 (2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.837\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecreatinine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.14 (44.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81.15 (59.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e68.39 (25.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e105.30 (4.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e105.03 (5.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105.51 (3.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.61 (1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.12 (2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.19 (0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.15 (1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.07 (0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.21 (1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efibrinogen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e452.17 (196.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e377.34 (233.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e513.51 (134.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.52 (3.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.44 (4.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.77 (3.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPTT-RATIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.01 (0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.98 (0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-dimer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e573.05 (656.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e612.28 (818.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e540.37 (485.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.587\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48.00 (43.24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.00 (70.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.00 (21.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes Mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.00 (19.82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.00 (16.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.00 (22.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.361\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026zwnj;Hypertension\u0026zwnj;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55.00 (49.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.00 (32.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.00 (63.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.00 (9.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.00 (4.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.00 (14.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtrial Fibrillation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00 (2.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbdominal muscle tension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.00 (14.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.00 (24.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.00 (6.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eepigastric pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.00 (31.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.00 (44.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.00 (21.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elower abdominal pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.00 (29.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.00 (12.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.00 (44.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eperiumbilical pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.00 (21.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.00 (18.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.00 (24.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.401\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft abdominal pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.00 (13.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.00 (4.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.00 (21.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRight abdominal pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.00 (11.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.00 (12.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.00 (11.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.932\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eflatulency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.00 (28.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.00 (16.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.00 (39.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emelena\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.00 (12.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.00 (14.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.00 (11.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.690\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003econstipated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.00 (2.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.00 (4.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eformless stool\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.00 (26.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00 (2.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.00 (45.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComputed Tomography\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48.00 (43.24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.00 (96.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntestinal wall edema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44.00 (39.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00 (2.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.00 (70.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThickening of intestinal wall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34.00 (30.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.00 (4.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.00 (52.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntestinal wall exudation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.00 (23.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00 (2.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.00 (40.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epneumatosis intestinalis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.00 (3.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.00 (4.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.00 (3.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.839\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAscites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.00 (2.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.00 (4.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etransverse colon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.00 (10.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00 (2.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.00 (18.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esplenic flexure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.00 (6.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.00 (11.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edescending colon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43.00 (38.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.00 (70.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esigmoid colon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.00 (23.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.00 (42.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esmall intestine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.00 (6.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.00 (14.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eSBP: Systolic Blood Pressure, WBC: White Blood Cell, MCV: Mean Corpuscular Volume, MCH: mean corpuscular hemoglobin, RDW: Red Blood Cell Distribution Width, PLT: Platelet, PCT: Plateletcrit, PDW: Platelet Distribution Width, ALT: Alanine Aminotransferase, PT: Prothrombin time, INR: International normalized ratio, APTT: Activated Partial Thromboplastin Time.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of 6 Machine Learning Models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUROC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAUROC CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAccuracy Cl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNegative Recal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePositive Recall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNegative F1-Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ePositive F1-Score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRandom forest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.903,0.940)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.819,0.883)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.880\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGaussian Naive Bayes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.770,0.872)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.532,0.830)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.690\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFully Connected Neural Network\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.902,0.932)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.824,0.877)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.840\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSupport Vector Machine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.899,0.937)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.819,0.894)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.860\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLogistic Regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.842,0.925)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.755,0.862)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGaussian Process\u003c/p\u003e\u003cp\u003eClassifier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.887,0.936)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.798,0.883)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.850\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eA predictive model for the early detection of IC has been successfully developed. Upon comparing various ML models, the RF model emerged as the most effective. After feature optimization, this model exhibited high predictive accuracy and strong generalization capabilities. Its applicability in real clinical environments was further assessed through external validation. The model demonstrates significant potential for aiding clinicians in the early identification of high-risk patients, facilitating timely interventions for the diagnosis of IC.\u003c/p\u003e\u003cp\u003eIn this study, the RF model exhibited superior performance, mainly attributed to its robust capabilities in processing nonlinear data and its high stability. When compared to other models, RF demonstrated exceptional proficiency in managing high-dimensional data, a critical factor when working with medical datasets that contain numerous features and variables. As shown in this study, RF achieved an AUC of 0.9251 and an accuracy of 0.8936 on the test set, reflecting its strong performance in classification tasks. Furthermore, RF offers significant advantages in dealing with missing data and mitigating overfitting\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. In contrast, while logistic regression offers better interpretability, it is less effective at processing nonlinear data. Despite the Gaussian process classification model marginally outperforming RF in AUC (0.9354 vs. 0.9251), it is associated with greater complexity in practical use, particularly due to its high computational demands when handling large-scale datasets\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Thus, RF strikes an optimal balance between performance and simplicity.\u003c/p\u003e\u003cp\u003eFeature optimization stands out as another significant aspect of this study. Initially, 99 features were used for model development and comparison, with the RF model emerging as the optimal selection. Using mutual information for feature ranking, performance testing was conducted across various feature thresholds, ultimately reducing the feature set to 21. This process notably simplified the model while maintaining its interpretability and applicability in clinical practice, with only a marginal loss in predictive accuracy. The streamlined model yielded a slightly lower AUC compared to the full-feature version (0.9191 vs. 0.9251), yet preserved a high level of accuracy (0.8510), thus striking an ideal balance between performance and feasibility\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. From a clinical standpoint, the reduced feature set enhances the model\u0026rsquo;s practicality for implementation, allowing healthcare providers to generate reliable predictions using only 21 essential features. This simplification not only eliminates the need for numerous difficult-to-obtain or redundant variables but also reduces the data collection burden and minimizes the risk of errors in clinical settings. Furthermore, DCA and CIC were employed to assess the model\u0026rsquo;s clinical decision-making utility. The results from DCA revealed significant net benefits across various thresholds, demonstrating the model\u0026rsquo;s potential for supporting clinical decisions at different levels of risk. This analysis effectively validated the model\u0026rsquo;s ability to reduce unnecessary interventions and prevent missed diagnoses\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Additionally, external validation results reinforced the model\u0026rsquo;s clinical relevance. The AUC of 0.9963 obtained from the external dataset confirmed the model\u0026rsquo;s generalizability, indicating strong performance not only within the original dataset but also across different hospital populations. This is particularly important, as model efficacy often varies markedly across medical institutions and patient demographics. External validation thus provided robust evidence supporting the model\u0026rsquo;s reliability and scalability\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough the model demonstrated strong performance in this study, several limitations warrant acknowledgment. First, while data from two hospitals were included, both hospitals were situated within the same province. To further substantiate the model\u0026rsquo;s applicability across broader contexts, large-scale, multi-center studies are necessary. Second, despite the optimization of features to reduce model complexity, the number of feature variables remains relatively high. Future investigations could focus on further simplification of the model or explore the inclusion of additional specific biomarkers to enhance its practical utility\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Lastly, although the RF model outperformed other models in terms of performance, its inherent \"black box\" nature poses a significant challenge. While the importance of various features was analyzed in this study, the model\u0026rsquo;s decision-making process remains challenging to interpret fully, which could potentially impact clinicians\u0026rsquo; confidence in its clinical implementation\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Future advancements might involve the integration of more transparent, interpretable models or the development of explanation tools to enhance the clinical acceptance of RF models.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAn early warning model for IC was successfully developed in this study utilizing the RF algorithm. Through feature optimization and subsequent model validation, the model\u0026rsquo;s predictive capabilities were confirmed, demonstrating its potential for clinical application and practicality. The model exhibits promising prospects for early IC diagnosis and necessitates further validation in large-scale clinical settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eACU\u003c/strong\u003e,area under the curve\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUROC\u003c/strong\u003e, area under the receiver operating characteristic\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e, confidence interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCIC\u003c/strong\u003e, clinical impact curves\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDCA\u003c/strong\u003e, decision curve analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIC\u003c/strong\u003e, ischemic colitis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eML\u003c/strong\u003e, machine learning\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRF\u003c/strong\u003e, random forest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROC\u003c/strong\u003e, receiver operating characteristic\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e, standard deviation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee in Clinical Research (ECCR) of the First Affiliated Hospital of Wenzhou Medical University (approval number: KY2024-R097) and conducted in accordance with the principles of the Declaration of Helsinki. Given the retrospective nature of the study, written informed consent was waived by the ethics committee. All procedures and analyses were performed in compliance with relevant guidelines and regulations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest to disclose in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by the Science and Technology Planning Project of Wenzhou City (Y2020127) and the National Key R\u0026amp;D Program of China (No. 2021YFC3002205).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMinzhe Lang: Writing the original draft, Investigation, Formal analysis, Methodology, Data curation, Conceptualization. Haoyue Hu: Formal analysis, Methodology, Investigation, Validation, Data curation. Minxuan Xu: Investigation, Data curation. Peiyuan Shou: Investigation, Data curation. Wenbin Chen: Validation, Data curation. Shaoce Zhi: Validation, Data curation. Guangliang Hong: Writing- review \u0026amp; editing, Funding acquisition, Conceptualization. Wenwen Li: Writing- review \u0026amp; editing, Validation, Formal analysis, Methodology, Supervision, Conceptualization. Xiaoqin Dai: Writing- review \u0026amp; editing, Validation, Formal analysis, Methodology, Supervision, Project administration, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGandhi SK, Hanson MM, Vernava AM, et al. Ischemic colitis[J]. Volume 39. Diseases of the colon \u0026amp; rectum; 1996. pp. 88\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoszkowicz D, Mariani A, Tr\u0026eacute;sallet C, et al. Ischemic colitis: the ABCs of diagnosis and surgical management[J]. J Visc Surg. 2013;150(1):19\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYadav S, Dave M, Varayil JE, et al. A population-based study of incidence, risk factors, clinical spectrum, and outcomes of ischemic colitis[J]. Clin Gastroenterol Hepatol. 2015;13(4):731\u0026ndash;8. e6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHines DM, McGuiness CB, Schlienger RG, et al. Incidence of ischemic colitis in treated, commercially insured hypertensive adults: a cohort study of US health claims data[J]. Am J Cardiovasc Drugs. 2015;15:135\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFitzGerald JF, Hernandez III. L O. Ischemic colitis[J]. 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Nat Mach Intell. 2019;1(5):206\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ischemic colitis, Machine learning, Random Forest, Early diagnosis, Predictive model","lastPublishedDoi":"10.21203/rs.3.rs-7020933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7020933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Ischemic colitis (IC), caused by reduced blood flow to the intestines, often presents with nonspecific early symptoms, leading to diagnostic delays and severe complications like necrosis or perforation. Current diagnostics (clinical evaluation, lab tests, imaging) lack sensitivity and specificity in early stages, highlighting the need for new predictive tools. This study proposes a machine learning model integrating clinical data, blood tests, and imaging descriptors to enable early IC detection at initial medical contact.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Data from IC patients’ initial visits (Oct 2015–Jun 2022, Wenzhou Medical University) were analyzed. Mutual information selected key features; six models (e.g., random forest, logistic regression) were built. The top-performing model was streamlined and externally validated using first-contact data from Ningbo Second Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The random forest model, derived from first medical contact data of 427 IC patients and 507 control patients, demonstrated the highest performance, achieving an area under the curve (AUC) of 0.9251 and an accuracy of 0.8936 in the test data set. The model, optimized with 21 critical features, showed an AUC of 0.9191 and an accuracy of 0.8510. External validation yielded an AUC of 0.9963 and an accuracy of 0.9369.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe RF-based IC model achieved superior diagnostic accuracy. Post-optimization, it maintained performance and demonstrated strong generalizability in external validation, underscoring its clinical utility.\u003c/p\u003e","manuscriptTitle":"Construction and Application of Early Warning Model for Ischemic Colitis in Emergency Patients Based on Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 16:17:32","doi":"10.21203/rs.3.rs-7020933/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c32f86db-8677-4f35-bd7e-f539045e7444","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-15T09:53:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-08 16:17:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7020933","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7020933","identity":"rs-7020933","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}