相关实验视频

可解释的机器学习用于与热有关的疾病预测:使用韩国气象数据的XGBoost-SHAP方法

Chaeyeong Im¹, Wonji Kim², Heesoo Kim^3,4

¹The Armed Forces Medical Command, Ministry of National Defense, Seongnam 13574, Gyeonggi-Do, Republic of Korea.

Bioengineering (Basel, Switzerland)

|November 27, 2025

概括

气候变化增加了与热有关的疾病 (HRIs). 这项研究使用可解释的机器学习 (ML) 来预测韩国城市的HRI风险,确定早期预警系统的关键天气因素.

科学领域:

环境健康环境健康
适应气候变化适应气候变化
公共卫生信息学公共卫生信息学

背景情况:

气候变化正在增加与热有关的疾病 (HRIs) 的频率,这给公共卫生带来了重大挑战,特别是在城市地区.
准确预测HRI风险对于及时的公共卫生干预和规划至关重要.

研究的目的:

开发和验证一种可解释的机器学习 (ML) 模型,用于预测每天的HRI风险.
使用可解释AI (XAI) 识别导致HRI风险的关键气象因素.
支持对气候敏感疾病的局部早期预警系统的开发.

主要方法:

应用了极端梯度增强 (XGBoost) 来基于每日气象数据 (温度,湿度,太阳辐射,风速,降水) 的HRI发生模型.
利用Shapley添加式解释 (SHAP) 进行模型解释,识别关键风险驱动因素.
使用历史数据和时间序列比较验证模型性能.

主要成果:

该ML模型显示了HRI风险的强大预测准确性 (AUC = 0.895).
平均每日温度,太阳辐射和最低温度被确定为HRI风险的最重要贡献者.
该模型有效地预测了现实世界的HRI事件.

结论:

可解释的人工智能 (XAI) 为局部健康风险预测提供了一种强大的方法.
开发的模型为积极的公共卫生规划提供了数据驱动的基础,以应对不断升级的城市热风险.
这些发现支持实施有针对性的预警系统,以减轻气候敏感疾病负担.

关键词:

沙普利添加剂的解释气候变化气候变化气候变化可解释的人工智能与热量相关的疾病.机器学习是机器学习.气象数据是气象数据.

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