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 pre...

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Detalles Bibliográficos
Autores principales: Kung, Benson, Chiang, Maurice, Perera, Gayan, Pritchard, Megan, Stewart, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society of Medical Informatics 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9388921/
https://www.ncbi.nlm.nih.gov/pubmed/35982600
http://dx.doi.org/10.4258/hir.2022.28.3.256

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9388921/
https://www.ncbi.nlm.nih.gov/pubmed/35982600
http://dx.doi.org/10.4258/hir.2022.28.3.256

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