This paper is in the following e-collection/theme issue:

Digital Health Reviews (1742) Machine Learning (2095) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (89) Depression and Mood Disorders; Suicide Prevention (1703) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2145) Intentional Self-Harm (138) Mental Health Issues in Adolescence (646) Artificial Intelligence (2162) Decision Support for Health Professionals (1555)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/73052, first published .
Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis

Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis

Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis

Authors of this article:

Lingjiang Liu1, 2 Author Orcid Image ;   Zhiyuan Li2, 3 Author Orcid Image ;   Yaxin Hu1, 2 Author Orcid Image ;   Chunyou Li1, 2 Author Orcid Image ;   Shuhan He1, 2 Author Orcid Image ;   Shibei Zhang1, 2 Author Orcid Image ;   Jie Gao2, 3 Author Orcid Image ;   Huaiyi Zhu1, 2 Author Orcid Image ;   Guoping Huang1, 2 Author Orcid Image
Article Authors Cited by (3) Tweetations (1) Metrics

Journals

  1. Kim S, Lee K. Machine learning-based prediction of suicide risk using adult attention-deficit/hyperactivity disorder symptoms and depression indicators: insights from a nationally representative south korean survey. Psychiatry Research 2025;352:116702 View
  2. Guo X, Liu S, Jiang L, Xiong Z, Wang L, Lu L, Li X, Zhao L, Shek D. Longitudinal machine learning prediction of non-suicidal self-injury among Chinese adolescents: A prospective multicenter Cohort study. Journal of Affective Disorders 2026;392:120110 View
  3. Duan Y, Lei C, Xu S, Wang J, Li J, Fu Y, Xi Y, An J, Chen R. Predicting perceived likelihood of future suicide attempts in youth with non-suicidal self-injury: A machine learning approach using LightGBM and SHAP. Journal of Affective Disorders 2025:120966 View