Machine Learning Prediction of Early Hypothermia in Sepsis Patients

Machine Learning Prediction of Early Hypothermia in Sepsis Patients | 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 Article Machine Learning Prediction of Early Hypothermia in Sepsis Patients Yali Chen, Ji Li, Yifeng Chen, Wenxue Tang, Shixiao Tu, JinXiu Huang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6399945/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 Sepsisis a systemic inflammatory response syndrome caused by infection, is a leading cause of high mortality worldwide. Abnormal body temperature, especially hypothermia (body temperature <36℃), is a key clinical feature in sepsis patients and is closely associated with disease severity, impaired immune function, and poor prognosis. Early prediction of hypothermia is crucial for timely intervention and improving prognosis. This study used machine learning algorithms to train and validate a prediction model for early temperature changes in critically ill sepsis patients. Data were extracted from the MIMIC-IV database and five models were established: XGBoost, LR, SVM, KNN, and ANN. The XGBoost model demonstrated the best predictive performance with AUC values of 0.92 in the training cohort and 0.98 in the validation cohort. This model can assist clinicians in identifying high-risk sepsis patients for early hypothermia and implementing early intervention to reduce mortality. Health sciences/Signs and symptoms Health sciences/Risk factors Hypothermia Sepsis Machine Learning Prediction Model MIMIC-IV Database Full Text 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6399945","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":534524753,"identity":"9f875168-d7fb-45a7-a1c6-26f2a58ddffa","order_by":0,"name":"Yali Chen","email":"","orcid":"","institution":"Affiliated Hospital of Hangzhou Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yali","middleName":"","lastName":"Chen","suffix":""},{"id":534524754,"identity":"4b16ab52-38e1-49be-8aa9-642e36f59d50","order_by":1,"name":"Ji 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