不确定性程度:在极端环境中进行非侵入性核心体温预测的合规深度学习
Joel Strickland1, Marco Ghisoni2, Hannah Marshall3,4
1Intellegens, The Studio, Chesterton Mill, Cambridge, UK. joel@intellegens.com.
Communications engineering
|November 20, 2025
概括
这项研究引入了一种符合规范的深度学习框架,用于准确的,非侵入性的核心体温 (CBT) 预测,以校准的不确定性. 新型号为热应激环境和可穿戴监控提供了更好的安全性.
科学领域:
- 生理监测是指对身体进行生理监测.
- 机器学习 机器学习
- 热应激研究研究热应激研究
背景情况:
- 准确的核心体温 (CBT) 估计对于生理监测至关重要.
- 目前的非侵入性方法缺乏统计学校准的不确定性,限制了安全关键的应用.
- 热应激等高风险环境需要可靠的CBT监控.
研究的目的:
- 开发一个实时的,非侵入性框架,用于用校准不确定性的CBT预测.
- 提高在苛刻条件下生理监测的安全性和可靠性.
- 为可靠,非侵入性监测系统创造一个可通用的基础.
主要方法:
- 通过使用超过14万个生理测量,开发了一个符合规范的深度学习框架.
- 该模型在六个操作领域进行了训练,包括高风险的热应激环境.
- 使用了可访问的生理,人口和环境输入.
主要成果:
- 该框架在CBT预测中实现了0.29°C的测试误差.
- 与ECTempTM相比,它在校准的概率准确度上得到了12倍的改善.
- 提供了统计学上有效的预测间隔和基于信心的监测.
结论:
- 合规深度学习框架提供可靠,非侵入性CBT预测与校准的不确定性.
- 该系统支持通过可定制的警报引擎进行实际监控,以进行主动干预.
- 这种方法适用于超出热应激场景的更广泛的安全关键生理监测.
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