[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125330-en":3,"doc-seo-125330-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125330,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Machine learning algorithms and their predictive accuracy for suicide and self-harm - Systematic review and meta-analysis","Rapid expansion in machine learning for predicting suicidal behaviours has outpaced clarity on real-world accuracy. This systematic review and meta-analysis evaluated how well machine learning algorithms predict suicide and hospital-treated self-harm using diagnostic-accuracy methods. Searches covered major biomedical and technical databases up to 30 April 2025. Fifty-three studies were eligible; AUROC was 0.69–0.93, sensitivity 45%–82%, specificity 91%–95%, positive likelihood ratios 6.5–9.9, and negative likelihood values 0.2–0.6.","OPEN ACCESS  \nCitation: Spittal MJ, Guo XA, Kang L, Kirtley OJ, Clapperton A, Hawton K, et al.(2025) Machine learning algorithms and their predictive accuracy for suicide and self-harm:  \nSystematic review and meta-analysis. PLoS Med 22(9): e1004581. [https://doi.org/10.1371/](https://doi.org/10.1371/)[ ](https://doi.org/10.1371/)[journal.pmed.1004581](journal.pmed.1004581)  \nAcademic Editor: Alexander C. Tsai, Massachusetts General Hospital, UNITED STATES OF AMERICA  \nReceived: February 10, 2025  \nAccepted: August 5, 2025  \nPublished: September 11, 2025  \nCopyright: © 2025 Spittal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData availability statement: The data and code used for the analyses are available to download from the Open Science Foundation. [https://doi](https://doi). org/10 . 17605/OSF. IO/KBRZV  \nRESEARCH ARTICLE  \nMachine learning algorithms and their predictive accuracy for suicide and self-harm: Systematic review and meta-analysis  \nMatthew J. Spittal1*, Xianglin Aneta Guo1, Laurant Kang2, Olivia J. Kirtley3, Angela Clapperton1, Keith Hawton4, Nav Kapur5,6,7, Jane Pirkis1, Greg Carter8,9  \n1 Centre for Mental Health and Community Wellbeing, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia, 2 Hunter New England Local Health District, Waratah, Australia, 3 Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium, 4 Centre for Suicide Research, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom, 5 National Confidential Inquiry into Suicide and Safety in Mental Health (NCISH), Centre for Mental Health and Safety, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom, 6 NIHR Greater Manchester Patient Safety Research Collaboration, University of Manchester, Manchester, United Kingdom, 7 Mersey Care NHS Foundation Trust, Liverpool, United Kingdom,  \n8 College of Health, Medicine and Wellbeing, School of Medicine and Public Health, The University of Newcastle, Callaghan, Australia, 9 Department of Consultation Liaison Psychiatry, Calvary Mater Newcastle Hospital, Waratah, Australia  \n* [m.spittal@unimelb.edu.au](m.spittal@unimelb.edu.au)  \nAbstract  \nBackground  \nThere has been rapid expansion in the development of machine learning algorithms to predict suicidal behaviours. To test the accuracy of these algorithms for predicting suicide and hospital-treated self-harm, we undertook a systematic review and meta-analysis. The study was registered (PROSPERO CRD42024523074) .  \nMethods and findings  \nWe searched PubMed, PsycINFO, Scopus, EMBASE, IEEE, Medline, CINALH and Web of Science from database inception until 30 April 2025 to identify studies using machine learning algorithms to predict suicide, self-harm and a combined suicide/ self-harm outcome. Studies were included if they examined suicide or hospitaltreated self-harm outcomes using a case-control, case-cohort or cohort study design. Studies were excluded if they used self-reported outcomes or examined outcomes using other study designs. Accuracy was assessed using statistical methods appropriate for diagnostic accuracy studies. Fifty-three studies met the inclusion criteria. The area under the receiver operating characteristic curves ranged from 0.69 to 0.93. Sensitivity was 45%–82% and specificity was 91%–95% . Positive likelihood ratios were 6.5–9.9 and negative likelihood values were 0.2–0.6. Using in-sample  \nPLOS Medicine | [https://doi.org/10.1371/journal.pmed.1004581](https://doi.org/10.1371/journal.pmed.1004581) September 11, 2025 1 / 23  \nFunding: The research was primarily funded by a National Health and Medical Research Council Investigator Grant to MS (grant reference GNT2025205, [https://www.nhmrc](https://www.nhmrc). 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