Machine learning and the prediction of suicide in psychiatric populations: a systematic review.

Publisher: Nature Pub. Group Country of Publication: United States NLM ID: 101562664 Publication Model: Electronic Cited Medium: Internet ISSN: 2158-3188 (Electronic) Linking ISSN: 21583188 NLM ISO Abbreviation: Transl Psychiatry Subsets: MEDLINE

Original Publication: New York, NY : Nature Pub. Group

Suicidal Ideation* ; Suicide, Attempted*; Humans ; Algorithms ; Machine Learning ; Risk Factors

Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, as many large-sample studies mixed psychiatric and non-psychiatric populations, a formal psychiatric diagnosis emerged as a strong predictor of suicidal risk, overshadowing more subtle risk factors specific to distinct populations. To overcome this limitation, we conducted a systematic review of ML studies evaluating suicidal behaviors exclusively in psychiatric clinical populations. A systematic literature search was performed from inception through November 17, 2022 on PubMed, EMBASE, and Scopus following the PRISMA guidelines. Original research using ML techniques to assess the risk of suicide or predict suicide attempts in the psychiatric population were included. An assessment for bias risk was performed using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. About 1032 studies were retrieved, and 81 satisfied the inclusion criteria and were included for qualitative synthesis. Clinical and demographic features were the most frequently employed and random forest, support vector machine, and convolutional neural network performed better in terms of accuracy than other algorithms when directly compared. Despite heterogeneity in procedures, most studies reported an accuracy of 70% or greater based on features such as previous attempts, severity of the disorder, and pharmacological treatments. Although the evidence reported is promising, ML algorithms for suicidal prediction still present limitations, including the lack of neurobiological and imaging data and the lack of external validation samples. Overcoming these issues may lead to the development of models to adopt in clinical practice. Further research is warranted to boost a field that holds the potential to critically impact suicide mortality.
(© 2024. The Author(s).)

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GR-2019-12369479 Ministero della Salute (Ministry of Health, Italy); Ricerca Corrente 2024 Ministero della Salute (Ministry of Health, Italy); Dipartimenti di Eccellenza Program 2023-2027 Ministero dell'Istruzione, dell'Università e della Ricerca (Ministry of Education, University and Research); 2019-3415 Fondazione Cariplo (Cariplo Foundation)

Date Created: 20240309 Date Completed: 20240311 Latest Revision: 20240416

20240417

PMC10925059

10.1038/s41398-024-02852-9

38461283