Unsupervised Machine Learning to Identify Depressive Subtypes

Unsupervised Machine Learning to Identify Depressive Subtypes

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 prese...

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Bibliographic Details
Main Authors: Benson Kung, Maurice Chiang, Gayan Perera, Megan Pritchard, Robert Stewart
Format: Article
Language:English
Published: The Korean Society of Medical Informatics 2022-07-01
Series:Healthcare Informatics Research
Subjects:
psychiatry
depression
mental health
machine learning
medical informatics
Online Access:http://www.e-hir.org/upload/pdf/hir-2022-28-3-256.pdf

Internet

http://www.e-hir.org/upload/pdf/hir-2022-28-3-256.pdf

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