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23 May 2005 Hybrid approaches to physiologic modeling and prediction
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Abstract
This paper explores how the accuracy of a first-principles physiological model can be enhanced by integrating data-driven, "black-box" models with the original model to form a "hybrid" model system. Both linear (autoregressive) and nonlinear (neural network) data-driven techniques are separately combined with a first-principles model to predict human body core temperature. Rectal core temperature data from nine volunteers, subject to four 30/10-minute cycles of moderate exercise/rest regimen in both CONTROL and HUMID environmental conditions, are used to develop and test the approach. The results show significant improvements in prediction accuracy, with average improvements of up to 30% for prediction horizons of 20 minutes. The models developed from one subject's data are also used in the prediction of another subject's core temperature. Initial results for this approach for a 20-minute horizon show no significant improvement over the first-principles model by itself.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nicholas O. Oleng' and Jaques Reifman "Hybrid approaches to physiologic modeling and prediction", Proc. SPIE 5797, Biomonitoring for Physiological and Cognitive Performance during Military Operations, (23 May 2005); https://doi.org/10.1117/12.605323
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