Semantically-aware population health risk analyses
This work addresses the need for explainable and dynamic risk factor discovery in population health, which is incremental as it builds on existing semantic and machine learning approaches.
The paper tackles the problem of identifying risk factors associated with health conditions in population health analysis by developing a combined semantic and machine learning system that uses a health risk ontology and knowledge graph to dynamically discover risk factors and subpopulations, resulting in an explainable system through semantics and a supervised cadre model.
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.