Learning a Health Knowledge Graph from Electronic Medical Records.

ABSTRACT: Demand for clinical decision support systems in medicine and self-diagnostic symptom checkers has substantially increased in recent years. Existing platforms rely on knowledge bases manually compiled through a labor-intensive process or automatically derived using simple pairwise statistics. This study explored an automated process to learn high quality knowledge bases linking diseases and symptoms directly from electronic medical records. Medical concepts were extracted from 273,174 de-identified patient records and maximum likelihood estimation of three probabilistic models was used to automatically construct knowledge graphs: logistic regression, naive Bayes classifier and a Bayesian network using noisy OR gates. A graph of disease-symptom relationships was elicited from the learned par

SUBMITTER: Rotmensch M 

PROVIDER: S-EPMC5519723 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature