A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data.

ABSTRACT:

Background

Identifying individuals at risk for future hospitalization or death has been a major priority of population health management strategies. High-risk individuals are a heterogeneous group, and existing studies describing heterogeneity in high-risk individuals have been limited by data focused on clinical comorbidities and not socioeconomic or behavioral factors. We used machine learning clustering methods and linked comorbidity-based, sociodemographic, and psychobehavioral data to identify subgroups of high-risk Veterans and study long-term outcomes, hypothesizing that factors other than comorbidities would characterize several subgroups.

Methods and findings

In this cross-sectional study, we used data from the VA Corporate Data Warehouse, a national repository of V

SUBMITTER: Parikh RB 

PROVIDER: S-EPMC7894856 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature