生物物理与机器学习模型用于预测老年人直肠和皮肤温度
Connor Forbes1, Alberto Coccarelli2, Zhiwei Xu3
1School of Information and Communication Technology, Griffith University, Gold Coast, Australia.
Journal of thermal biology
|February 26, 2025
概括
机器学习模型,特别是回归模型,比生物物理模型更准确地预测老年人暴露在热中的体温. 这为个性化热风险评估和实时监控提供了潜力.
科学领域:
- 环境健康 环境健康
- 计算生物学 计算生物学
- 老年学是一门学科.
背景情况:
- 由于温度调节的改变,老年人容易受到热应激.
- 准确预测体温对于评估热风险至关重要 在这个人群中.
- 传统的生物物理模型在捕捉人类对热的复杂反应方面存在局限性.
研究的目的:
- 为了比较机器学习 (ML) 模型与传统生物物理模型的预测效果,在热暴露期间对老年人直肠 (Tre) 和皮肤 (Tsk) 温度进行预测.
- 评估特定ML模型 (如回归) 和生物物理模型 (JOS-3,Gagge两节点,优化两节点) 的性能.
主要方法:
- 训练了五个ML模型,使用4倍交叉验证对162天长的会议与76名老年人 (≥60岁) 的数据进行了验证.
- 将ML模型与三个已建立的生物物理模型在各种热环境中进行比较,从热中性到热浪条件.
- 使用根-平均平方误差 (RMSE) 评估预测准确性和临床上有意义的温度值内的参与者的比例.
主要成果:
- 斜坡回归显示出优异的预测准确性,Tre的RMSE达到0.27°C,Tsk的RMSE达到0.73°C.
- 机器学习模型,特别是回归,在预测Tre和Tsk方面表现优于生物物理模型.
- 脊柱回归实现了临床相关RMSE值Tre (70%) 和Tsk (88%) 的最高比例的参与者.
结论:
- 机器学习模型,特别是回归模型,在预测热暴露期间老年人热调节反应方面比生物物理模型更有效.
- ML模型显示出开发针对弱势群体的个性化热风险评估和实时热监测系统的巨大潜力.
- 未来的研究应该专注于扩大数据集,纳入动态条件,并在现实环境中验证ML模型,以增强热管理策略.
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