  One primary task of population health analysis is the identification of risk
factors that, for some subpopulation, have a significant association with some
health condition. Examples include finding lifestyle factors associated with
chronic diseases and finding genetic mutations associated with diseases in
precision health. We develop a combined semantic and machine learning system
that uses a health risk ontology and knowledge graph (KG) to dynamically
discover risk factors and their associated subpopulations. Semantics and the
novel supervised cadre model make our system explainable. Future population
health studies are easily performed and documented with provenance by
specifying additional input and output KG cartridges.

Machine Learning;Artificial Intelligence;Machine Learning