Degrees of Uncertainty: Conformal Deep Learning for Core Body Temperature Prediction in Extreme Environments

Degrees of Uncertainty: Conformal Deep Learning for Core Body Temperature Prediction in Extreme Environments | 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 Degrees of Uncertainty: Conformal Deep Learning for Core Body Temperature Prediction in Extreme Environments Joel Strickland, Marco Ghisoni, Hannah Marshall, Thomas Whitehead, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5705569/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Nov, 2025 Read the published version in Communications Engineering → Version 1 posted You are reading this latest preprint version Abstract We introduce a conformal deep learning framework for real-time, non-invasive core body temperature (CBT) prediction, designed for deployment in diverse, high-risk heat environments. The framework combines bidirectional Long Short-Term Memory (LSTM) networks with dense layers and leverages Stratification of Inductive Conformal Prediction Estimates (SCOPE) to deliver reliable uncertainty calibration and adaptability across varied demographics, workloads, protective gear, and environmental conditions. Calibrated over a CBT range of 36.15°C to 40.25°C, the model delivers a test error of 0.29°C, outperforming the widely used ECTemp™, with a 12-fold improvement in probabilistic accuracy and statistically guaranteed prediction intervals. Designed for seamless integration with wearable devices, the framework leverages accessible demographic, physiological, and environmental metrics for practical, non-invasive monitoring. SCOPE powers a customizable alert system, enabling proactive safety management through precise, real-time interventions. Adaptable to user-defined thresholds and confidence levels, the system is well-suited for applications in healthcare, industry, and other high-risk settings prone to heat stress. By combining accurate predictions with reliable uncertainty estimates, our framework sets a new standard for non-invasive, real-time CBT monitoring in safety-critical applications. Health sciences/Health occupations Biological sciences/Physiology/Metabolism/Homeostasis Physical sciences/Mathematics and computing/Computational science Health sciences/Risk factors Full Text Additional Declarations There is NO Competing Interest. author has confirmed that they would like to preprint. Cite Share Download PDF Status: Published Journal Publication published 20 Nov, 2025 Read the published version in Communications Engineering → 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-5705569","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":398713987,"identity":"14ad1b4a-43c9-44bd-a309-461b9b160edf","order_by":0,"name":"Joel 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