Unsupervised Machine Learning to Identify Depressive Subtypes.

ABSTRACT:

Objectives

This study evaluated an unsupervised machine learning method, latent Dirichlet allocation (LDA), as a method for identifying subtypes of depression within symptom data.

Methods

Data from 18,314 depressed patients were used to create LDA models. The outcomes included future emergency presentations, crisis events, and behavioral problems. One model was chosen for further analysis based upon its potential as a clinically meaningful construct. The associations between patient groups created with the final LDA model and outcomes were tested. These steps were repeated with a commonly-used latent variable model to provide additional context to the LDA results.

Results

Five subtypes were identified using the final LDA model. Prior to the outcome analysis, the subty

SUBMITTER: Kung B 

PROVIDER: S-EPMC9388921 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature