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.
@article{arxiv.1811.11190,
title = {Semantically-aware population health risk analyses},
author = {Alexander New and Sabbir M. Rashid and John S. Erickson and Deborah L. McGuinness and Kristin P. Bennett},
journal= {arXiv preprint arXiv:1811.11190},
year = {2018}
}
Comments
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:cs/0101200