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Prediction Models for Suicide Attempts among Adolescents Using Machine Learning Techniques.

Jae Seok Lim1, Chan-Mo Yang2,3, Ju-Won Baek4

  • 1Department of Oral and Maxillofacial Surgery, Chungbuk National University Hospital, Cheongju, Korea.

Clinical Psychopharmacology and Neuroscience : the Official Scientific Journal of the Korean College of Neuropsychopharmacology
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PubMed
Summary

Machine learning models accurately predict adolescent suicide attempts using survey data. These models can identify at-risk youth for early intervention and suicide prevention efforts.

Keywords:
AdolescentsAttempted suicideMachine learningSuicide

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Area of Science:

  • Adolescent mental health
  • Machine learning in public health
  • Suicide prevention research

Background:

  • Suicide attempts (SAs) are a critical public health issue among adolescents, representing a leading cause of death.
  • Predicting SAs in adolescents is challenging, necessitating advanced analytical approaches.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) models for predicting SAs in Korean adolescents.
  • To utilize data from the Korea Youth Risk Behavior Survey (KYRBS) for model training and validation.

Main Methods:

  • Utilized data from 468,482 Korean adolescents (ages 12-18) from the 2011-2018 KYRBS.
  • Trained and tested six ML algorithms on internal and external datasets, assessing performance using AUROC and AUPRC metrics.
  • Identified key predictors including suicidal ideation, suicide planning, and grade level.

Main Results:

  • The study identified 15,012 cases (3.2%) of SAs among participants.
  • ML models demonstrated strong performance on internal datasets (AUROC: 0.92-0.94, AUPRC: 0.92-0.94).
  • External validation showed high AUROC (0.93-0.95) but moderate AUPRC (approx. 0.5), indicating potential for targeted risk detection.

Conclusions:

  • Developed ML models show promise for identifying adolescents at high risk for SAs.
  • These predictive models can support early intervention strategies.
  • The application of these models may contribute to adolescent suicide prevention efforts.