ArticleClinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology2022
Prediction Models for Suicide Attempts among Adolescents Using Machine Learning Techniques.
Article in Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Evaluating the role of cost and fairness in machine learning models of suicide risk.Scientific reports · 2026Article
- Predicting mental health risk in adolescents using interpretable machine learning: Evidence from SHAP analysis.PloS one · 2026Article
- Development and Validation of a Nomogram for Estimating the Risk of Suicide Tendencies Among Chinese Middle School Students: A Cross-Sectional Study.Psychology research and behavior management · 2026Article
- Understanding the emergence of suicidal thoughts and behaviors in adolescence from a brain and behavioral developmental perspective.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Considerations for evaluating the practical utility of machine learning in suicide risk estimation: the role of cost and equity.Research square · 2025Article
- Parental Mental Health and Suicidal Behavior as Predictors of Adolescent Suicidal Ideation and Attempts: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- Use of Artificial Intelligence in Adolescents' Mental Health Care: Systematic Scoping Review of Current Applications and Future Directions.JMIR mental health · 2025Article
- Behavioral and social predictors of suicidal ideation and attempts among adolescents and young adults.PLOS mental health · 2025Article
- Exploration of a Machine Learning Model Using Self-rating Questionnaires for Detecting Depression in Patients with Breast Cancer.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2024Article
- Predicting Suicidal Ideation, Planning, and Attempts among the Adolescent Population of the United States.Healthcare (Basel, Switzerland) · 2024Article
- Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2024Article
- Predicting adolescent suicidal behavior following inpatient discharge using structured and unstructured data.Journal of affective disorders · 2024Article
- Risk Factors and Trends in Adolescent's Suicide Attempt Rates Before and After the Coronavirus Disease 2019 Pandemic.Journal of Korean medical science · 2024Article
- Peripheral Markers of Suicidal Behavior: Current Findings and Clinical Implications.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2023Review
- Classification of Adolescent Psychiatric Patients at High Risk of Suicide Using the Personality Assessment Inventory by Machine Learning.Psychiatry investigation · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Suicide attempts (SAs) in adolescents are difficult to predict although it is a leading cause of death among adolescents. This study aimed to develop and evaluate SA prediction models based on six different machine learning (ML) algorithms for Korean adolescents using data from online surveys. Methods: Data were extracted from the 2011-2018 Korea Youth Risk Behavior Survey (KYRBS), an ongoing annual national survey. The participants comprised 468,482 nationally representative adolescents from 400 middle and 400 high schools, aged 12 to 18. The models were trained using several classic ML methods and then tested on internal and external independent datasets; performance metrics were calculated. Data analysis was performed from March 2020 to June 2020. Results: Among the 468,482 adolescents included in the analysis, 15,012 cases (3.2%) were identified as having made an SA. Three features (suicidal ideation, suicide planning, and grade) were identified as the most important predictors. The performance of the six ML models on the internal testing dataset was good, with both the area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) ranging from 0.92 to 0.94. Although the AUROC of all models on the external testing dataset (2018 KYRBS) ranged from 0.93 to 0.95, the AUPRC of the models was approximately 0.5. Conclusion: The developed and validated SA prediction models can be applied to detect high risks of SA. This approach could facilitate early intervention in the suicide crisis and may ultimately contribute to suicide prevention for adolescents.
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