The performance of machine learning models in predicting suicidal ideation, attempts, and deaths: A meta-analysis and systematic review.

Kusuma, Karen; Larsen, Mark; Quiroz, Juan C; Gillies, Malcolm; Burnett, Alexander; Qian, Jiahui; Torok, Michelle

Kusuma, Karen; Larsen, Mark; Quiroz, Juan C; Gillies, Malcolm; Burnett, Alexander; Qian, Jiahui; Torok, Michelle.

Afiliação

Kusuma K; Black Dog Institute, University of New South Wales, Hospital Road, Randwick NSW, 2031, Australia. Electronic address: k.kusuma@unsw.edu.au.
Larsen M; Black Dog Institute, University of New South Wales, Hospital Road, Randwick NSW, 2031, Australia.
Quiroz JC; Centre for Big Data Research in Health, University of New South Wales, Sydney NSW, 2052, Australia.
Gillies M; Centre for Big Data Research in Health, University of New South Wales, Sydney NSW, 2052, Australia.
Burnett A; Black Dog Institute, University of New South Wales, Hospital Road, Randwick NSW, 2031, Australia.
Qian J; Black Dog Institute, University of New South Wales, Hospital Road, Randwick NSW, 2031, Australia.
Torok M; Black Dog Institute, University of New South Wales, Hospital Road, Randwick NSW, 2031, Australia.

J Psychiatr Res ; 155: 579-588, 2022 11.

Article em En | MEDLINE | ID: mdl-36206602

Assuntos

Ideação Suicida; Suicídio; Área Sob a Curva; Humanos; Aprendizado de Máquina; Tentativa de Suicídio

Palavras-chave

Algorithm; Artificial intelligence; Machine learning; Predictive modelling; Suicide risk assessment; meta-Analysis

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Suicídio / Ideação Suicida Tipo de estudo: Prognostic_studies / Risk_factors_studies / Systematic_reviews Limite: Humans Idioma: En Revista: J Psychiatr Res Ano de publicação: 2022 Tipo de documento: Article

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