ArticlePloS one2021
Predicting suicidal thoughts and behavior among adolescents using the risk and protective factor framework: A large-scale machine learning approach.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Modeling suicidal thoughts and behaviors in university students: integrating suicide theory with interpretable machine learning.BMC psychiatry · 2026Article
- Suicidal ideation in adolescents: the role of personal and school variables.BMC psychology · 2026Article
- Developmental trajectories of vocabulary and logical thinking skills across adolescence: a person-centered longitudinal study in China.BMC public health · 2026Article
- An interpretable machine learning model for predicting sleep disturbances in adolescents with mood disorders: the key role of psychological factors.BMC psychiatry · 2025Article
- Suicides in India: A Forecasting for Immediate Investment to Save Lives.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine · 2025Article
- Predictive Model of Suicidal Ideation Among Children and Adolescents in Pediatric Outpatient Settings.Child psychiatry and human development · 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
- The risk factors for the comorbidity of depression and self-injury in adolescents: a machine learning study.European child & adolescent psychiatry · 2025Article
- Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques.BMC psychiatry · 2025Article
- Use of Artificial Intelligence in Adolescents' Mental Health Care: Systematic Scoping Review of Current Applications and Future Directions.JMIR mental health · 2025Article
- Development and validation of a predictive model for suicidal thoughts and behaviors among freshmen.BMC psychiatry · 2025Article
- Predicting bullying victimization among adolescents using the risk and protective factor framework: a large-scale machine learning approach.BMC public health · 2025Article
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- Machine Learning-Based Prediction of Suicidal Thinking in Adolescents by Derivation and Validation in 3 Independent Worldwide Cohorts: Algorithm Development and Validation Study.Journal of medical Internet research · 2024Article
- Exploring the most discriminative brain structural abnormalities in ASD with multi-stage progressive feature refinement approach.Frontiers in psychiatry · 2024Article
- Examining the relationship between risky sexual behavior and suicidal thoughts among unmarried adolescents in India.Scientific reports · 2023Article
- Impact of COVID-19 on the mental health of adolescents and youth in Nairobi, Kenya.Frontiers in psychiatry · 2023Article
- Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences.Science advances · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAddressing the problem of suicidal thoughts and behavior (STB) in adolescents requires understanding the associated risk factors. While previous research has identified individual risk and protective factors associated with many adolescent social morbidities, modern machine learning approaches can help identify risk and protective factors that interact (group) to provide predictive power for STB. This study aims to develop a prediction algorithm for STB among adolescents using the risk and protective factor framework and social determinants of health.
methodsThe sample population consisted of more than 179,000 high school students living in Utah and participating in the Communities That Care (CTC) Youth Survey from 2011-2017. The dataset includes responses to 300+ questions from the CTC and 8000+ demographic factors from the American Census Survey for a total of 1.2 billion values. Machine learning techniques were employed to extract the survey questions that were best able to predict answers indicative of STB, using recent work in interpretable machine learning.
resultsAnalysis showed strong predictive power, with the ability to predict individuals with STB with 91% accuracy. After extracting the top ten questions that most affected model predictions, questions fell into four main categories: familial life, drug consumption, demographics, and peer acceptance at school.
conclusionsModern machine learning approaches provide new methods for understanding the interaction between root causes and outcomes, such as STB. The model developed in this study showed significant improvement in predictive accuracy compared to previous research. Results indicate that certain risk and protective factors, such as adolescents being threatened or harassed through digital media or bullied at school, and exposure or involvement in serious arguments and yelling at home are the leading predictors of STB and can help narrow and reaffirm priority prevention programming and areas of focused policymaking.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.