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An overview of clustering methods with guidelines for application in mental health research.
Gao, Caroline X; Dwyer, Dominic; Zhu, Ye; Smith, Catherine L; Du, Lan; Filia, Kate M; Bayer, Johanna; Menssink, Jana M; Wang, Teresa; Bergmeir, Christoph; Wood, Stephen; Cotton, Sue M.
Afiliación
  • Gao CX; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia; Department of Epidemiology and Preventative Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia. Electronic address: caroline
  • Dwyer D; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia.
  • Zhu Y; School of Information Technology, Deakin University, Geelong, VIC, Australia.
  • Smith CL; Department of Epidemiology and Preventative Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
  • Du L; Faculty of Information Technology, Monash University, Clayton, VIC, Australia.
  • Filia KM; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia.
  • Bayer J; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia.
  • Menssink JM; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia.
  • Wang T; Faculty of Information Technology, Monash University, Clayton, VIC, Australia.
  • Bergmeir C; Faculty of Information Technology, Monash University, Clayton, VIC, Australia; Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain.
  • Wood S; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia.
  • Cotton SM; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia.
Psychiatry Res ; 327: 115265, 2023 09.
Article en En | MEDLINE | ID: mdl-37348404
Cluster analyzes have been widely used in mental health research to decompose inter-individual heterogeneity by identifying more homogeneous subgroups of individuals. However, despite advances in new algorithms and increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements. In this paper, we aimed to address this gap by introducing the philosophy, design, advantages/disadvantages and implementation of major algorithms that are particularly relevant in mental health research. Extensions of basic models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently introduced. How to choose algorithms to address common issues as well as methods for pre-clustering data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general guidance on clustering workflow and reporting requirements. To facilitate the implementation of different algorithms, we provide information on R functions and libraries.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Salud Mental Tipo de estudio: Guideline Límite: Humans Idioma: En Revista: Psychiatry Res Año: 2023 Tipo del documento: Article Pais de publicación: Irlanda
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Salud Mental Tipo de estudio: Guideline Límite: Humans Idioma: En Revista: Psychiatry Res Año: 2023 Tipo del documento: Article Pais de publicación: Irlanda