构建一个小时通风模型,以解决在使用可穿戴设备的相同暴露场景中的个人间吸入剂量变化
Pengpeng Wu1, Qian Guo2, Yuchen Zhao1
1School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
The Science of the total environment
|September 23, 2024
概括
准确的空气污染暴露评估需要预测每分钟通风 (VE). 机器学习模型,特别是随机森林,准确地预测VE,改善吸入剂量计算,揭示健康影响,特别是在女性中.
科学领域:
- 环境健康 环境健康
- 肺部生理学 肺部生理学
- 机器学习在健康中的应用
背景情况:
- 准确的吸入剂量评估对于了解空气污染对健康的影响至关重要.
- 现有的每分钟通风 (VE) 预测模型不足以进行暴露评估.
- 机器学习为开发强大的 VE 预测模型提供了潜力.
研究的目的:
- 开发和验证用于预测每分钟通风 (VE) 的机器学习模型.
- 应用这些模型来评估空气污染暴露对健康的影响.
- 确定影响 VE 预测的关键变量.
主要方法:
- 利用了从80名接受心肺运动测试 (CPET) 的参与者的数据.
- 开发并比较了通用添加模型 (GAM),随机森林 (RF) 和极端梯度增强 (XGBoost) 以进行 VE 预测.
- 使用可解释的人工智能来识别重要的预测变量.
主要成果:
- 随机森林模型实现了高精度 (R2=0.986,MAE=1.816 L/分钟).
- 预测VE与测量VE非常相匹配 (中位数差异为0.18L/分钟).
- 黑碳 (BC) 吸入剂量显示出与女性的扩张性血压 (DBP) 有显著的不良关联.
结论:
- 机器学习,特别是射频,提供了准确的 VE 预测模型.
- 代谢等效任务 (METs),心率和体重是VE的关键预测因素.
- 使用预测VE进行精确的吸入剂量评估,揭示了污染物特定的健康影响,突出了基于性别的差异.
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