你的位置如何与健康相关：面向环境与社会人口数据的变量重要性及可解释机器学习
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data
AI总结：
该研究利用 MEDSAT 数据集，通过变量重要性方法及基于 GAM 与 MGWR 的可解释机器学习框架，识别多种健康结果的全局与局部环境、社会人口预测因子。
摘要：
健康结果取决于复杂的环境和社会人口因素，这些因素的影响会随位置和时间而变化。直到最近，用于研究这些影响的细粒度空间和时间数据才得以获得，即包含英格兰健康、环境和社会人口信息的 MEDSAT 数据集。利用这一新资源，我们采用多种变量重要性技术，在多种健康结果中稳健地识别最具信息量的预测因子。随后，我们开发了一个基于广义加性模型（GAM）和多尺度地理加权回归（MGWR）的可解释机器学习框架，以分析每个变量对各类健康结果的局部和全局空间依赖。我们的发现将 NO2 确定为哮喘、高血压和焦虑的全局预测因子，此外还有与职业、婚姻和植被相关的其他结果特异性预测因子。区域分析揭示了空气污染和太阳辐射的局部变化，并在 COVID 期间出现显著转变。这一综合方法为解决健康差异提供了可行动的洞见，并倡导将可解释机器学习纳入公共卫生。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
