  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.
