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

Journal: Journal of medical Internet research
Published Date: Jun 16, 2025

Abstract

BACKGROUND: In the context of escalating global mental health challenges, adolescent suicide has become a critical public health concern. In current clinical practices, considerable challenges are encountered in the early identification of suicide risk, as traditional assessment tools demonstrate limited predictive accuracy. Recent advancements in machine learning (ML) present promising solutions for risk prediction. However, comprehensive evaluations of their efficacy in adolescent populations remain insufficient.

Authors

  • Lingjiang Liu
    Department of Psychiatry, North Sichuan Medical College, Nanchong, China.
  • Zhiyuan Li
    School of Clinical Medicine, General Hospital of Ningxia Medical University, Yinchuan, China.
  • Yaxin Hu
    Institute of Neuro- and Bioinformatics, University of Luebeck, Ratzeburger Allee 160, Luebeck, 23562, Schleswig-Holstein, Germany; Pattern Recognition Company GmbH, Maria-Goeppert-Straße 3, Luebeck, 23562, Schleswig-Holstein, Germany. Electronic address: yh@prcmail.de.
  • Chunyou Li
    Department of Psychiatry, North Sichuan Medical College, Nanchong, China.
  • Shuhan He
    Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA. Electronic address: She8@partners.org.
  • Shibei Zhang
    Department of Psychiatry, North Sichuan Medical College, Nanchong, China.
  • Jie Gao
    Department of Nephrology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Huaiyi Zhu
    Department of Psychiatry, North Sichuan Medical College, Nanchong, China.
  • Guoping Huang
    Department of Psychiatry, North Sichuan Medical College, Nanchong, China.

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