ArticleFrontiers in psychiatry2023
Predicting suicide attempts among Norwegian adolescents without using suicide-related items: a machine learning approach.
Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Temporal maintenance of an adolescent suicide attempt screening score in national surveys: An observational study.Medicine · 2026Observational
- Diagnostic accuracy of machine learning approaches for suicide‑related outcomes: a meta‑analysis.Annals of general psychiatry · 2026Article
- Predicting suicide attempts in a high-risk clinical cohort of adolescents using machine-learning.European child & adolescent psychiatry · 2026Article
- Development and External Validation of a Prediction Model to Identify Suicide Attempters in Treatment-Naive Adolescents With Major Depressive Disorder.Depression and anxiety · 2026Article
- Predictive modeling of adolescent suicidal behavior using machine learning: Key features and algorithmic insights.MethodsX · 2025Review
- 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
- Prediction of Adolescent Suicide Attempt by Integrating Clinical, Neurocognitive and Geocoded Neighborhood Environment Data.Schizophrenia bulletin · 2025Article
- Behavioral and social predictors of suicidal ideation and attempts among adolescents and young adults.PLOS mental health · 2025Article
- Predicting Suicidal Ideation, Planning, and Attempts among the Adolescent Population of the United States.Healthcare (Basel, Switzerland) · 2024Article
- Analysis and evaluation of explainable artificial intelligence on suicide risk assessment.Scientific reports · 2024Article
- Unveiling Adolescent Suicidality: Holistic Analysis of Protective and Risk Factors Using Multiple Machine Learning Algorithms.Journal of youth and adolescence · 2024Article
- Do early-life circumstances predict late-life suicidal ideation? Evidence from SHARE data using machine learning.Frontiers in psychiatry · 2024Article
- Are false positives in suicide classification models a risk group? Evidence for "true alarms" in a population-representative longitudinal study of Norwegian adolescents.Frontiers in psychology · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Research on the classification models of suicide attempts has predominantly depended on the collection of sensitive data related to suicide. Gathering this type of information at the population level can be challenging, especially when it pertains to adolescents. We addressed two main objectives: (1) the feasibility of classifying adolescents at high risk of attempting suicide without relying on specific suicide-related survey items such as history of suicide attempts, suicide plan, or suicide ideation, and (2) identifying the most important predictors of suicide attempts among adolescents. Methods: Nationwide survey data from 173,664 Norwegian adolescents (ages 13-18) were utilized to train a binary classification model, using 169 questionnaire items. The Extreme Gradient Boosting (XGBoost) algorithm was fine-tuned to classify adolescent suicide attempts, and the most important predictors were identified. Results: XGBoost achieved a sensitivity of 77% with a specificity of 90%, and an AUC of 92.1% and an AUPRC of 47.1%. A coherent set of predictors in the domains of internalizing problems, substance use, interpersonal relationships, and victimization were pinpointed as the most important items related to recent suicide attempts. Conclusion: This study underscores the potential of machine learning for screening adolescent suicide attempts on a population scale without requiring sensitive suicide-related survey items. Future research investigating the etiology of suicidal behavior may direct particular attention to internalizing problems, interpersonal relationships, victimization, and substance use.
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