title: Regularization of body core temperature prediction during physical activity.
abstract: This paper deals with the prediction of body core temperature during physical activity in different environmental conditions using first-principles models and data-driven models. We argue that prediction of physiological variables through other correlated physiological variables using data-driven techniques is an ill-posed problem. To make predictions produced by data-driven models accurate and stable they need to be regularized. We demonstrate on data collected during laboratory study that data-driven models, if regularized properly, can outperform first-principles models in terms of accuracy of core temperature predictions. We also show that data-driven models can be made "portable" from one subject to another, thus, making them a valuable, practical tool when data from only one subject is available to "train" the model.
authors.affiliation: Bioinformatics Cell, Telemedicine & Advanced Technology Research Center, US Army Medical Research & Material Command, Frederick, MD 21702, USA. agribok@bioanalysis.org
authors.forename: Andrei
authors.initials: A
authors.lastname: Gribok
authors.forename: Thomas
authors.initials: T
authors.lastname: McKenna
authors.forename: Jacques
authors.initials: J
authors.lastname: Reifman
mesh_terms: D000328:Adult; D001831:Body Temperature / Q000502:physiology*; D003198:Computer Simulation; D004735:Energy Transfer / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D009043:Motor Activity / Q000502:physiology*; D005082:Physical Exertion / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity