Unsupervised Learning for Automated Detection of Coronary Artery Disease Subgroups.

ABSTRACT: Background The promise of precision population health includes the ability to use robust patient data to tailor prevention and care to specific groups. Advanced analytics may allow for automated detection of clinically informative subgroups that account for clinical, genetic, and environmental variability. This study sought to evaluate whether unsupervised machine learning approaches could interpret heterogeneous and missing clinical data to discover clinically important coronary artery disease subgroups. Methods and Results The Genetic Determinants of Peripheral Arterial Disease study is a prospective cohort that includes individuals with newly diagnosed and/or symptomatic coronary artery disease. We applied generalized low rank modeling and K-means cluster analysis using 155 phenotypic a

SUBMITTER: Flores AM 

PROVIDER: S-EPMC9075403 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature