Degrees of uncertainty: conformal deep learning for non-invasive core body temperature prediction in extreme environments.

ABSTRACT: Accurate estimation of core body temperature (CBT) is essential for physiological monitoring, yet current non-invasive methods lack statistically calibrated uncertainty estimates required for safety-critical use. Here we introduce a conformal deep learning framework for real-time, non-invasive CBT prediction with calibrated uncertainty, demonstrated in high-risk heat-stress environments. Developed from over 140,000 physiological measurements across six operational domains, the model achieves a test error of 0.29 °C, outperforming the widely used ECTemp™ algorithm with a 12-fold improvement in calibrated probabilistic accuracy and statistically valid prediction intervals. Designed for integration with wearable devices, the system uses accessible physiological, demographic, and environmental

SUBMITTER: Strickland J 

PROVIDER: S-EPMC12727793 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature