Machine learning algorithms and their predictive accuracy for suicide and self-harm: Systematic review and meta-analysis.

ABSTRACT:

Background

There 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).

Methods and findings

We 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 hospital-treated self-harm outcomes using a case-control, case-cohort or cohort study design. Studies were excluded if they used self

SUBMITTER: Spittal MJ 

PROVIDER: S-EPMC12425223 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature