Detecting time-evolving phenotypic topics via tensor factorization on electronic health records: Cardiovascular disease case study.

ABSTRACT:

Objective

Discovering subphenotypes of complex diseases can help characterize disease cohorts for investigative studies aimed at developing better diagnoses and treatments. Recent advances in unsupervised machine learning on electronic health record (EHR) data have enabled researchers to discover phenotypes without input from domain experts. However, most existing studies have ignored time and modeled diseases as discrete events. Uncovering the evolution of phenotypes - how they emerge, evolve and contribute to health outcomes - is essential to define more precise phenotypes and refine the understanding of disease progression. Our objective was to assess the benefits of an unsupervised approach that incorporates time to model diseases as dynamic processes in phenotype discovery.

SUBMITTER: Zhao J 

PROVIDER: S-EPMC6783385 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature