A machine learning framework for modeling the associations between environmental features, individual characteristics and health: an application in the German National Cohort

Kurzfassung

Human health has been associated with the exposure to several environmental variables, socio-economic and neighborhood settings, as well as individual-level characteristics. Yet, their interplay is not adequately analyzed and consequently not well understood. Our results indicate some variation in performance and that a guided application is needed if evidence shall be generated beyond major drivers of disease such as age and sex. The ML pipeline shall be openly accessible soon for use in epidemiological analysis, specifically with binary health outcomes. We are expanding the ML pipeline to continuous outcomes.