Comparative Evaluation of Ensemble Machine Learning Models for Predicting Antimicrobial Resistance from Electronic Health Records

Comparative Evaluation of Ensemble Machine Learning Models for Predicting Antimicrobial Resistance from Electronic Health Records | 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 Comparative Evaluation of Ensemble Machine Learning Models for Predicting Antimicrobial Resistance from Electronic Health Records Zohoor Almalki, Amjad Althagafi, Sarah Al-Shareef This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7829893/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract Antimicrobial resistance (AMR), the ability of microbes to survive exposure to drugs intended to eliminate them, is a critical global health concern exacerbated by the overuse and misuse of antibiotics. In this study, we leverage machine learning techniques to predict AMR and evaluate the performance of several advanced supervised algorithms. Using the Antibiotic Resistance Microbiology Dataset (ARMD), a detailed electronic health record (EHR) dataset containing rich clinical, demographic, microbiological, and treatment data from Stanford Healthcare [ 1 , 2 ]. Our approach involves robust data preprocessing to predict the likelihood that a patient’s bacterial iso- late responds to a specific antibiotic as either resistant or susceptible, based on clinical characteristics, microbiological findings, treatment history, and demographic information. We compare the perfor- mance of state-of-the-art machine learning models, including XGBoost, LightGBM, Random Forest, and HistGradientBoostingClassifier, in building reliable predictive models of antibiotic susceptibility. By benchmarking these models on a large real-world dataset, this research identifies effective pre- dictive strategies that can support antimicrobial stewardship, enhance clinical decision-making, and contribute to addressing the growing challenge of AMR. antimicrobial resistance electronic health records machine learning gradient boosting random forest clinical decision support Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Feb, 2026 Reviews received at journal 24 Feb, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviews received at journal 24 Feb, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers agreed at journal 15 Feb, 2026 Reviews received at journal 28 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviews received at journal 18 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviews received at journal 21 Nov, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviewers invited by journal 18 Nov, 2025 Editor invited by journal 11 Nov, 2025 Editor assigned by journal 06 Nov, 2025 Submission checks completed at journal 29 Oct, 2025 First submitted to journal 29 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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-7829893","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":548863854,"identity":"eab95149-e109-4781-95f1-89d4f65182cc","order_by":0,"name":"Zohoor 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