Trial reportPsychiatry research2020
Using Machine Learning to Predict Suicide Attempts in Military Personnel.
Trial report in Psychiatry research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 4 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
14 citing papers in PubMed, 4 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.Psychological medicine · 2025Pooled it
- The impact of machine learning in predicting risk of violence: A systematic review.Frontiers in psychiatry · 2022Pooled 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
- Development and internal validation of machine learning models for identifying suicidal behavior in inpatients with mood disorders.Frontiers in psychiatry · 2026Article
- Building an early warning model for the risk of suicide attempt in people with depression based on machine learning: a single-centre study.Frontiers in psychiatry · 2026Article
- Construction and Initial Psychometric Validation of the Morana Scale: A Multidimensional Projective Tool Developed Using AI-Generated Illustrations.Journal of clinical medicine · 2025Article
- Machine learning applications related to suicide in military and Veterans: A scoping literature review.Journal of biomedical informatics · 2025Article
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- Predicting Lifetime Suicide Attempts in a Community Sample of Adolescents Using Machine Learning Algorithms.Assessment · 2024Article
- Development and Validation of a Machine Learning Prediction Model of Posttraumatic Stress Disorder After Military Deployment.JAMA network open · 2023Article
- 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
- Classification of Adolescent Psychiatric Patients at High Risk of Suicide Using the Personality Assessment Inventory by Machine Learning.Psychiatry investigation · 2021Article
- Courses of suicidal ideation among military veterans in residential treatment for posttraumatic stress disorder.Depression and anxiety · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 7 institutions in 2 countries.
Funding
Abstract
Identifying predictors of suicide attempts is critical in intervention and prevention efforts, yet finding predictors has proven difficult due to the low base rate and underpowered statistical approaches. The objective of the current study was to use machine learning to examine predictors of suicidal behaviors among high-risk suicidal Soldiers who received outpatient mental health services in a randomized controlled trial of Brief Cognitive Behavioral Therapy for Suicide Prevention (BCBT) compared to treatment as usual (TAU). Self-report measures of clinical and demographic variables, administered prior to the start of outpatient treatment to 152 participants with recent suicidal thoughts and/or behaviors were analyzed using machine learning software to identify the best combination of variables for predicting suicide attempts during or after treatment. Worst-point suicidal ideation, history of multiple suicide attempts, treatment group (i.e., BCBT or TAU), suicidogenic cognitions, and male sex were found, in combination, correctly classified 30.8% of patients who attempted suicide during the two-year follow-up period. This combination has higher sensitivity than many models that have previously been used to predict suicidal behavior. Overall, this study provides a combination of variables that can be assessed clinical to help identify high-risk suicidal individuals.
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.