Clinical Report: Predicting Adolescent Suicide Attempts Using Machine Learning

Overview

This study developed machine learning models to predict adolescent suicide attempts using data from the Korea Youth Risk Behavior Survey. The models demonstrated moderate predictive performance, with psychological and social variables being the strongest predictors.

Background

Adolescent suicide attempts are a significant public health concern, representing a leading cause of death in this age group. Early identification of at-risk adolescents is crucial for effective prevention and intervention strategies. Machine learning approaches may enhance prediction accuracy by capturing complex relationships among various risk factors.

Data Highlights

Key Findings

• Machine learning models showed moderate predictive performance for adolescent suicide attempts.
• XGBoost achieved the highest F1 score among the models evaluated.
• Random forest demonstrated the highest sensitivity under screening conditions.
• Hopelessness was identified as the strongest predictor of suicide attempts.
• Other significant predictors included school violence, perceived stress, self-rated health, and household economic status.
• Positive predictive values remained low despite improved sensitivity under screening conditions.

Clinical Implications

The findings suggest that machine learning models can aid in identifying adolescents at risk for suicide attempts, emphasizing the importance of psychological and social factors in risk assessment. Clinicians may consider integrating these models into routine screening practices.

Conclusion

Machine learning models provide a promising approach to predicting adolescent suicide attempts, highlighting the role of various psychological and social predictors. Further research is needed to enhance their predictive validity and clinical utility.

Related Resources & Content

1. Author(s)/Org, Source, Year -- Predicting Adolescent Suicide Attempts Using Machine Learning
2. Author(s)/Org, Source, Year -- Development and validation of a machine learning–based risk prediction model for non-suicidal self-injury in adolescents
3. Author(s)/Org, Source, Year -- Implication of machine learning models versus traditional models for the prediction of suicidal thoughts or ideation in west of Iran
4. Author(s)/Org, Source, Year -- Suicide risk assessment: clinical implications of the unpredictability of suicidal behavior
5. American Academy of Pediatrics, AAP News, Year -- Suicide risk in adolescents: Updated report guides pediatricians through screening, intervention
6. CDC, Mental Health, Year -- Suicidal Thoughts & Behavior
7. CDC Youth Risk Behavior Survey
8. Suicide risk in adolescents: Updated report guides pediatricians through screening, intervention | AAP News | American Academy of Pediatrics
9. Journal of Medical Internet Research - Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis