ArticleScientific reports2021
A machine learning approach for predicting suicidal thoughts and behaviours among college students.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Scalable prediction of suicidal risk in university students: a three steps machine learning approach in university settings.Scientific reports · 2026Article
- Modeling suicidal thoughts and behaviors in university students: integrating suicide theory with interpretable machine learning.BMC psychiatry · 2026Article
- A scoping review of the use of artificial intelligence as a psychological assessment tool.Translational psychiatry · 2026Article
- Predictive modeling of adolescent suicidal behavior using machine learning: Key features and algorithmic insights.MethodsX · 2025Review
- Concurrent and Prospective Prediction of Suicidal Ideation in Adolescents Using Multimethod Data and Machine Learning: A Pilot Study.JAACAP open · 2025Article
- Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques.BMC psychiatry · 2025Article
- Development and validation of a predictive model for suicidal thoughts and behaviors among freshmen.BMC psychiatry · 2025Article
- Machine learning based identification of suicidal ideation using non-suicidal predictors in a university mental health clinic.Scientific reports · 2025Article
- Factors Associated With Suicidal Ideation in College Students of Health Sciences.Depression and anxiety · 2025Article
- Predicting suicidality in people living with HIV in Uganda: a machine learning approach.Frontiers in psychiatry · 2025Article
- WeNet-RF: An Automatic Classification Model for Financial Reimbursement Budget Items.PloS one · 2025Article
- Behavioral and social predictors of suicidal ideation and attempts among adolescents and young adults.PLOS mental health · 2025Article
- Prediction of non-suicidal self-injury (NSSI) among rural Chinese junior high school students: a machine learning approach.Annals of general psychiatry · 2024Article
- Predicting Lifetime Suicide Attempts in a Community Sample of Adolescents Using Machine Learning Algorithms.Assessment · 2024Article
- Article
- Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort.Nature. Mental health · 2024Article
- Article
- Association of parental death and illness with offspring suicidal ideation: cross-sectional study in a large cohort of university students.Social psychiatry and psychiatric epidemiology · 2022Article
- Telehealth-Supported Decision-making Psychiatric Care for Suicidal Ideation: Longitudinal Observational Study.JMIR formative research · 2022Article
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7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Suicidal thoughts and behaviours are prevalent among college students. Yet little is known about screening tools to identify students at higher risk. We aimed to develop a risk algorithm to identify the main predictors of suicidal thoughts and behaviours among college students within one-year of baseline assessment. We used data collected in 2013-2019 from the French i-Share cohort, a longitudinal population-based study including 5066 volunteer students. To predict suicidal thoughts and behaviours at follow-up, we used random forests models with 70 potential predictors measured at baseline, including sociodemographic and familial characteristics, mental health and substance use. Model performance was measured using the area under the receiver operating curve (AUC), sensitivity, and positive predictive value. At follow-up, 17.4% of girls and 16.8% of boys reported suicidal thoughts and behaviours. The models achieved good predictive performance: AUC, 0.8; sensitivity, 79% for girls, 81% for boys; and positive predictive value, 40% for girls and 36% for boys. Among the 70 potential predictors, four showed the highest predictive power: 12-month suicidal thoughts, trait anxiety, depression symptoms, and self-esteem. We identified a parsimonious set of mental health indicators that accurately predicted one-year suicidal thoughts and behaviours in a community sample of college students.
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