ArticlePsychiatry investigation2019
Detection of Suicide Attempters among Suicide Ideators Using Machine Learning.
Article in Psychiatry investigation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- 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
- Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis.Frontiers in digital health · 2022Pooled it
- Advantages and disadvantages of artificial intelligence in the prediction and prevention of suicide.Postepy psychiatrii neurologii · 2026Review
- Article
- Integrating thyroid function and psychometric profiles for lifetime suicide-attempt risk stratification in bipolar disorder: A multi-algorithm machine-learning study.Frontiers in psychiatry · 2026Article
- Optimizing Suicide Risk Prediction in Korea: A Comparison of Model Performance Using Resampling Methods and Machine Learning Algorithms.Psychiatry investigation · 2025Article
- Using Machine Learning and the HAMD-24 Scale to Predict Suicide Ideation in Depressed Patients.Psychology research and behavior management · 2025Article
- Analysis and evaluation of explainable artificial intelligence on suicide risk assessment.Scientific reports · 2024Article
- Exploring the Role of First-Person Singular Pronouns in Detecting Suicidal Ideation: A Machine Learning Analysis of Clinical Transcripts.Behavioral sciences (Basel, Switzerland) · 2024Article
- Artificial intelligence and suicide prevention: a systematic review.European psychiatry : the journal of the Association of European Psychiatrists · 2022Review
- Leveraging data science to enhance suicide prevention research: a literature review.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2022Review
- Prediction model for suicide based on back propagation neural network and multilayer perceptron.Frontiers in neuroinformatics · 2022Article
- College students' screening early warning factors in identification of suicide risk.Frontiers in genetics · 2022Article
- A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining.Diagnostics (Basel, Switzerland) · 2021Review
- AI enabled suicide prediction tools: a qualitative narrative review.BMJ health & care informatics · 2020Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveWe aimed to develop predictive models to identify suicide attempters among individuals with suicide ideation using a machine learning algorithm.
methodsAmong 35,116 individuals aged over 19 years from the Korea National Health & Nutrition Examination Survey, we selected 5,773 subjects who reported experiencing suicide ideation and had answered a survey question about suicide attempts. Then, we performed resampling with the Synthetic Minority Over-sampling TEchnique (SMOTE) to obtain data corresponding to 1,324 suicide attempters and 1,330 non-suicide attempters. We randomly assigned the samples to a training set (n=1,858) and a test set (n=796). In the training set, random forest models were trained with features selected through recursive feature elimination with 10-fold cross validation. Subsequently, the fitted model was used to predict suicide attempters in the test set.
resultsIn the test set, the prediction model achieved very good performance [area under receiver operating characteristic curve (AUC)=0.947] with an accuracy of 88.9%.
conclusionOur results suggest that a machine learning approach can enable the prediction of individuals at high risk of suicide through the integrated analysis of various suicide risk factors.
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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.