Computational Phenotype Discovery via Probabilistic Independence

Abstract

Computational Phenotype Discovery research has taken various pragmatic approaches to disentangling phenotypes from the episodic observations in Electronic Health Records. In this work, we use transformation into continuous, longitudinal curves to abstract away the sparse irregularity of the data, and we introduce probabilistic independence as a guiding principle for disentangling phenotypes into patterns that may more closely match true pathophysiologic mechanisms. We use the identification of liver disease patterns that presage development of Hepatocellular Carcinoma as a proof-of-concept demonstration.