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

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Machine learning algorithms show low accuracy for predicting suicidal behaviors and hospital-treated self-harm. Current methods are insufficient for screening or prioritizing interventions, necessitating alternative clinical approaches.

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Area of Science:

  • Computational psychiatry
  • Clinical informatics
  • Public health research

Background:

  • Rapid advancements in machine learning (ML) for predicting suicidal behaviors.
  • Need to systematically evaluate ML algorithm accuracy for suicide and hospital-treated self-harm prediction.

Purpose of the Study:

  • To conduct a systematic review and meta-analysis of ML algorithms predicting suicide and hospital-treated self-harm.
  • To assess the diagnostic accuracy of these ML algorithms.

Main Methods:

  • Systematic search of multiple databases (PubMed, PsycINFO, Scopus, etc.) until April 2025.
  • Inclusion of case-control, case-cohort, or cohort studies on suicide or hospital-treated self-harm.
  • Exclusion of studies with self-reported outcomes or other designs.
  • Statistical assessment of accuracy using methods for diagnostic accuracy studies.

Main Results:

  • Fifty-three studies met inclusion criteria.
  • Area under the ROC curve ranged from 0.69 to 0.93.
  • Sensitivity: 45%-82%; Specificity: 91%-95%; Positive Likelihood Ratio: 6.5-9.9.
  • Positive predictive values varied significantly with population prevalence (0.1% to 66%).

Conclusions:

  • ML algorithm accuracy for predicting suicidal behavior is currently too low for effective screening or intervention prioritization.
  • For hospital-treated self-harm, management should focus on needs-based assessment, addressing modifiable risk factors, and effective aftercare.