Subphenotyping depression using machine learning and electronic health records.

ABSTRACT:

Objective

To identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications.

Materials and methods

Using EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics.

Results

Using the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster o

SUBMITTER: Xu Z 

PROVIDER: S-EPMC7556423 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature