[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128020-en":3,"doc-seo-128020-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128020,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Interpretable Machine Learning-Based Prediction of 28-Day Mortality in ICU Patients With Sepsis - A Multicenter Retrospective Study","Sepsis remains a major cause of ICU mortality and a persistent global health challenge. This multicenter retrospective study develops and evaluates a machine learning model to forecast 28-day all-cause mortality in ICU patients with sepsis. Data were drawn from the eICU Collaborative Research Database and predictors were selected using LASSO regression and Boruta feature selection. Model performance was assessed via multiple classification metrics with 10-fold cross-validation, and interpretability analysis was performed on the most stable model to support clinical insight.","TYPE Original Research PUBLISHED 08 January 2025  \nDOI 10.3389/fcimb.2024.1500326  \nOPEN ACCESS  \nEDITED BY  \nGang Ye,  \nSichuan Agricultural University, China  \nREVIEWED BY  \nNozomi Takahashi,  \nUniversity of British Columbia, Canada Kawther Alquadan,  \nUniversity of Florida, United States Bin Yi,  \nArmy Medical University, China Jie Weng,  \nSecond Affiliated Hospital and Yuying Children ’s Hospital of Wenzhou Medical University, China  \n*CORRESPONDENCE  \nZhiping Li  \n [zpli@fudan.edu.cn](zpli@fudan.edu.cn)[ ](zpli@fudan.edu.cn)Yi Wang  \n[yiwang@shmu.edu.cn](yiwang@shmu.edu.cn)  \n†These authors share first authorship  \nRECEIVED 23 September 2024  \nACCEPTED 16 December 2024  \nPUBLISHED 08 January 2025  \nCITATION  \nShen L, Wu J, Lan J, Chen C, Wang Y and Li Z (2025) Interpretable machine learning-based prediction of 28-day mortality in ICU patients with sepsis: a multicenter retrospective study. Front. Cell. Infect. Microbiol. 14:1500326 .  \ndoi: 10.3389/fcimb.2024.1500326  \nCOPYRIGHT  \n© 2025 Shen, Wu, Lan, Chen, Wang and Li.  \nThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nInterpretable machine learningbased prediction of 28-day mortality in ICU patients with sepsis: a multicenter retrospective study  \nLi Shen 1,2†, Jiaqiang Wu 3†, Jianger Lan 1†, Chao Chen 4, Yi Wang 5* and Zhiping Li 1*  \n1 Department of Clinical Pharmacy, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, China, 2 Department of Pharmacy, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, Jiangsu, China, 3School of Life Sciences and Biopharmaceutical Science, Shenyang Pharmaceutical University, Shenyang, China, 4 Department of Neonatology, Children ’s Hospital of Fudan University, National Children ’s Medical Center, Shanghai, China, 5 Department of Neurology, Children ’s Hospital of Fudan University, National Children ’s Medical Center, Shanghai, China  \nBackground: Sepsis is a major cause of mortality in intensive care units (ICUs) and continues to pose a significant global health challenge, with sepsis-related deaths contributing substantially to the overall burden on healthcare systems worldwide. The primary objective was to construct and evaluate a machine learning (ML) model for forecasting 28-day all-cause mortality among ICU sepsis patients.  \nMethods: Data for the study was sourced from the eICU Collaborative Research Database (eICU-CRD) (version 2 . 0) . The main outcome was 28-day all-cause mortality. Predictor selection for the final model was conducted using the least absolute shrinkage and selection operator (LASSO) regression analysis and the Boruta feature selection algorithm. Five machine learning algorithms including logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), support vector machine (SVM), and light gradient boosting machine (lightGBM) were employed to construct models using 10-fold cross-validation. Model performance was evaluated using AUC, accuracy, sensitivity, specificity, recall, and F1 score. Additionally, we performed an interpretability analysis on the model that showed the most stable performance.  \nResults: The final study cohort comprised 4564 patients, among whom 568 (12 . 4%) died within 28 days of ICU admission . The XGBoost algorithm demonstrated the most reliable performance, achieving an AUC of 0.821, balancing sensitivity (0 . 703) and specificity (0 . 798) . The top three risk predictors of mortality included APACHE score, serum lactate levels, and AST.  \nFrontiers in Cellular and Infection Microbiology 01 [frontiersin.org](","cbCairrKrJyb2HKu","https://ap.wps.com/l/cbCairrKrJyb2HKu","pdf",3045459,2,1,12,"English","en",105,"# Introduction\n## Background and clinical problem\n# Methods\n## Data source and outcomes\n## Feature selection and model building\n## Performance evaluation and interpretability\n# Results\n## Cohort description\n## Model comparison and key predictors\n# Conclusion","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To construct and evaluate a machine learning model that forecasts 28-day all-cause mortality among ICU sepsis patients.\"},{\"question\":\"Which dataset and outcome were used?\",\"answer\":\"The study used data from the eICU Collaborative Research Database (version 2.0). The primary outcome was 28-day all-cause mortality.\"},{\"question\":\"How were features selected and models validated?\",\"answer\":\"Predictors were selected using LASSO regression and the Boruta feature selection algorithm. Five ML algorithms were trained with 10-fold cross-validation and evaluated using AUC, accuracy, sensitivity, specificity, recall, and F1 score.\"}]","Interpretable Machine Learning-Based Prediction of 28-Day Mortality in ICU Patients With Sepsis - A Multicenter Retrospective Study | PDF",1785943974,30,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"interpretable-machine-learning-based-prediction-of-28-day-mortality-in-icu-patients-with-sepsis-a-multicenter-retrospective-study","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/interpretable-machine-learning-based-prediction-of-28-day-mortality-in-icu-patients-with-sepsis-a-multicenter-retrospective-study/128020/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main objective of the study?","Question",{"text":76,"@type":77},"To construct and evaluate a machine learning model that forecasts 28-day all-cause mortality among ICU sepsis patients.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which dataset and outcome were used?",{"text":81,"@type":77},"The study used data from the eICU Collaborative Research Database (version 2.0). The primary outcome was 28-day all-cause mortality.",{"name":83,"@type":74,"acceptedAnswer":84},"How were features selected and models validated?",{"text":85,"@type":77},"Predictors were selected using LASSO regression and the Boruta feature selection algorithm. 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