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This study sought to (1) expand our existing Urban Population HealthObservatory (UPHO) system by incorporating a semantics layer; (2) cohesivelyemploy machine learning and semantic/logical inference to provide measurableevidence and detect pathways leading to undesirable health outcomes; (3)provide clinical use case scenarios and design case studies to identifysocioenvironmental determinants of health associated with the prevalence ofobesity, and (4) design a dashboard that demonstrates the use of UPHO in thecontext of obesity surveillance using the provided scenarios. The system designincludes a knowledge graph generation component that provides contextualknowledge from relevant domains of interest. This system leverages semanticsusing concepts, properties, and axioms from existing ontologies. In addition,we used the publicly available US Centers for Disease Control and Prevention500 Cities data set to perform multivariate analysis. A cohesive approach thatemploys machine learning and semantic/logical inference reveals pathwaysleading to diseases. In this study, we present 2 clinical case scenarios and aproof-of-concept prototype design of a dashboard that provides warnings,recommendations, and explanations and demonstrates the use of UPHO in thecontext of obesity surveillance, treatment, and prevention. While exploring thecase scenarios using a support vector regression machine learning model, wefound that poverty, lack of physical activity, education, and unemployment werethe most important predictive variables that contribute to obesity in Memphis,TN. The application of UPHO could help reduce health disparities and improveurban population health. The expanded UPHO feature incorporates an additionallevel of interpretable knowledge to enhance physicians, researchers, and healthofficials' informed decision-making at both patient and community levels.

jmir formative research

An Urban Population Health Observatory for Disease Causal Pathway  Analysis and Decision Support: Underlying Explainable Artificial Intelligence  Model