Unsupervised Machine Learning to Identify Depressive Subtypes.

Content Provider	Europe PMC
Author	Kung, Benson Chiang, Maurice Perera, Gayan Pritchard, Megan Stewart, Robert
Copyright Year	2022
Abstract	ObjectivesThis study evaluated an unsupervised machine learning method, latent Dirichlet allocation (LDA), as a method for identifying subtypes of depression within symptom data.MethodsData 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.ResultsFive subtypes were identified using the final LDA model. Prior to the outcome analysis, the subtypes were labeled based upon the symptom distributions they produced: psychotic, severe, mild, agitated, and anergic-apathetic. The patient groups largely aligned with the outcome data. For example, the psychotic and severe subgroups were more likely to have emergency presentations (odds ratio [OR] = 1.29; 95% confidence interval [CI], 1.17–1.43 and OR = 1.16; 95% CI, 1.05–1.29, respectively), whereas these outcomes were less likely in the mild subgroup (OR = 0.86; 95% CI, 0.78–0.94). We found that the LDA subtypes were characterized by clusters of unique symptoms. This contrasted with the latent variable model subtypes, which were largely stratified by severity.ConclusionsThis study suggests that LDA can surface clinically meaningful, qualitative subtypes. Future work could be incorporated into studies concerning the biological bases of depression, thereby contributing to the development of new psychiatric therapeutics.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9388921&blobtype=pdf
ISSN	20933681
Journal	Healthcare Informatics Research [Healthc Inform Res]
Volume Number	28
DOI	10.4258/hir.2022.28.3.256
PubMed Central reference number	PMC9388921
Issue Number	3
PubMed reference number	35982600
e-ISSN	2093369X
Language	English
Publisher	Korean Society of Medical Informatics
Publisher Date	2022-07-31
Access Restriction	Open
Rights License	This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2022 The Korean Society of Medical Informatics
Subject Keyword	Psychiatry Depression Mental Health Machine Learning Medical Informatics
Content Type	Text
Resource Type	Article
Subject	Health Information Management Biomedical Engineering Health Informatics