Year: 2019
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Lasko, Thomas A., Mesa, Diego A.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2963620734