Gribok Andrei V, Buller Mark J, Reifman Jaques
Bioinformatics Cell, Telemedicine and Advanced Technology Research Center (TATRC), US Army Medical Research and Materiel Command (USAMRMC), Fort Detrick, MD 21702, USA.
IEEE Trans Biomed Eng. 2008 May;55(5):1477-87. doi: 10.1109/TBME.2007.913990.
This study compares and contrasts the ability of three different mathematical modeling techniques to predict individual-specific body core temperature variations during physical activity. The techniques include a first-principles, physiology-based (SCENARIO) model, a purely data-driven model, and a hybrid model that combines first-principles and data-driven components to provide an early, short-term (20-30 min ahead) warning of an impending heat injury. Their performance is investigated using two distinct datasets, a Field study and a Laboratory study. The results indicate that, for up to a 30 min prediction horizon, the purely data-driven model is the most accurate technique, followed by the hybrid. For this prediction horizon, the first-principles SCENARIO model produces root mean square prediction errors that are twice as large as those obtained with the other two techniques. Another important finding is that, if properly regularized and developed with representative data, data-driven and hybrid models can be made "portable" from individual to individual and across studies, thus significantly reducing the need for collecting developmental data and constructing and tuning individual-specific models.
本研究比较并对比了三种不同数学建模技术预测体育活动期间个体特异性体核温度变化的能力。这些技术包括基于第一原理、生理学的（情景）模型、纯数据驱动模型以及结合第一原理和数据驱动组件以提供即将发生热损伤的早期短期（提前20 - 30分钟）预警的混合模型。使用两个不同的数据集（一项实地研究和一项实验室研究）对它们的性能进行了调查。结果表明，对于长达30分钟的预测期，纯数据驱动模型是最准确的技术，其次是混合模型。对于此预测期，基于第一原理的情景模型产生的均方根预测误差是其他两种技术的两倍。另一个重要发现是，如果使用代表性数据进行适当正则化和开发，数据驱动模型和混合模型可以在个体之间以及跨研究实现“可移植”，从而显著减少收集发育数据以及构建和调整个体特异性模型的需求。
