用可解释模型分析位置如何影响健康，发现空气污染是哮喘等病的全球风险因素。
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data
- 结合GAM与多尺度地理加权回归，同时捕捉全局与局部空间关系。
- 识别出NO2对哮喘、高血压、焦虑症具有全球显著影响，且疫情期变化明显。
- 适合公共卫生政策制定者和城市规划者参考，推动数据驱动的健康公平决策。
健康结果受复杂环境与社会人口因素影响，其作用随地理位置和时间变化。近期可用的细粒度时空数据（如英格兰的MEDSAT数据集）使研究成为可能。我们采用多种变量重要性方法，稳健识别多个健康结果中最关键的预测因子，并构建基于广义加性模型（GAMs）与多尺度地理加权回归（MGWR）的可解释机器学习框架，分析各变量对不同健康结果的局部与全局空间依赖性。结果显示，NO2对哮喘、高血压和焦虑症具有全局预测作用，其他因素则因病种而异，包括职业、婚姻状况及植被覆盖。区域分析揭示空气污染与太阳辐射在局部存在差异，且在新冠疫情期间出现显著变化。该综合方法为应对健康不平等提供了可操作的洞见，倡导将可解释机器学习纳入公共卫生实践。
原文摘要 · Abstract (English)
Health outcomes depend on complex environmental and sociodemographic factors whose effects change over location and time. Only recently has fine-grained spatial and temporal data become available to study these effects, namely the MEDSAT dataset of English health, environmental, and sociodemographic information. Leveraging this new resource, we use a variety of variable importance techniques to robustly identify the most informative predictors across multiple health outcomes. We then develop an interpretable machine learning framework based on Generalized Additive Models (GAMs) and Multiscale Geographically Weighted Regression (MGWR) to analyze both local and global spatial dependencies of each variable on various health outcomes. Our findings identify NO2 as a global predictor for asthma, hypertension, and anxiety, alongside other outcome-specific predictors related to occupation, marriage, and vegetation. Regional analyses reveal local variations with air pollution and solar radiation, with notable shifts during COVID. This comprehensive approach provides actionable insights for addressing health disparities, and advocates for the integration of interpretable machine learning in public health.
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