Predicting the Optimal Insulin Dose and Controlling the Blood Glucose Levels with Deep Reinforcement Learning

Predicting the Optimal Insulin Dose and Controlling the Blood Glucose Levels with Deep Reinforcement 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 Predicting the Optimal Insulin Dose and Controlling the Blood Glucose Levels with Deep Reinforcement Learning Panagiotis Symeonidis, Evangelos C. Rizos, Dimosthenis Sotiriou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4989455/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 This paper addresses the critical challenge of predicting future insulin dosages for diabetic patientsin Intensive Care Units (ICUs), a setting where precise glycemic control is crucial. The work proposes aunique methodology that sidesteps the complications associated with traditional glucose-insulin interactionmodels, due to data sparsity. We introduce a simplified version of Bergman’s Insulin-Glucose Interaction Modeland construct an extended dataset based on the MIMIC III database. This dataset includes 870 predictivefeatures encompassing demographic data, prior insulin administrations, and average glucose levels. The paperalso introduces a Reinforcement Learning approach, utilizing Deep Q-Learning, to optimize both the thetraining population and the feature selection for individualized predictions. We found that our CompositeMultilinear Regression Model algorithm outperforms the single-patient regression model in terms of MeanAbsolute Error (MAE). Specifically, the MAE values for the Type I and Type II Diabetic groups were 2.33 and3.68, respectively, significantly better than the single-patient regression model. The work contributes a novelapproach to insulin dose prediction, offering a promising pathway for more effective glucose management inICU settings. 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. 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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-4989455","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":348403502,"identity":"9a45a9c8-57b8-47f5-b56c-646a2c4ae26e","order_by":0,"name":"Panagiotis Symeonidis","email":"","orcid":"","institution":"University of the Aegean","correspondingAuthor":false,"prefix":"","firstName":"Panagiotis","middleName":"","lastName":"Symeonidis","suffix":""},{"id":348403503,"identity":"4f63c36c-294c-49df-b594-b82bdb6e5d92","order_by":1,"name":"Evangelos C. 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