SynthesisPloS one2021
A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis.
Synthesis in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Prediction models for self-harm and suicide: a systematic review and critical appraisal.BMC medicine · 2025Pooled it
- The Application of AI to Ecological Momentary Assessment Data in Suicide Research: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Occupational Health Applied Infodemiological Studies of Nutritional Diseases and Disorders: Scoping Review with Meta-Analysis.Nutrients · 2023Pooled it
- Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning.Frontiers in psychiatry · 2023Pooled it
- Modeling suicidal thoughts and behaviors in university students: integrating suicide theory with interpretable machine learning.BMC psychiatry · 2026Article
- Integrating neurobiological markers to prospectively predict adolescent non-suicidal self-injury and suicide attempts: a machine learning approach.Child and adolescent psychiatry and mental health · 2026Article
- Diagnostic accuracy of machine learning approaches for suicide‑related outcomes: a meta‑analysis.Annals of general psychiatry · 2026Article
- Marital Status, Social Integration, and Suicidal Thoughts and Behaviors in the Military Health and Well-Being Project.Journal of clinical psychology · 2026Article
- Comparing patterns of recent mental health service use for predicting suicidal events following emergency department mental health visits in the United States: A national cohort study.Social psychiatry and psychiatric epidemiology · 2026Article
- A biological phenotype of suicide attempt in adolescents with nonsuicidal self-injury: a machine-based learning approach.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025Article
- Contribution of Chronic Disease in Predicting Depression and Suicidal Ideation Among the Older Adult Population.Psychiatry investigation · 2025Article
- Suicidality Screening: Ensuring Providers Do Not Miss Adolescents at High Risk through Clinical Decision Support.Applied clinical informatics · 2025Article
- Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach.Epidemiology and psychiatric sciences · 2025Article
- Evaluating Machine Learning for Predicting Youth Suicidal Behavior Up to 1 Year After Contact With Mental-Health Specialty Care.Clinical psychological science : a journal of the Association for Psychological Science · 2025Article
- Let's Move Towards Precision Suicidology.Current psychiatry reports · 2025Review
- Machine learning based identification of suicidal ideation using non-suicidal predictors in a university mental health clinic.Scientific reports · 2025Article
- Dynamic impacts of sleep disruption on ecologically assessed affective, behavioral, and cognitive risk factors for suicide: a study protocol.Sleep advances : a journal of the Sleep Research Society · 2025Article
- Improving explainability of post-separation suicide attempt prediction models for transitioning service members: insights from the Army Study to Assess Risk and Resilience in Servicemembers - Longitudinal Study.Translational psychiatry · 2025Article
- An Explainable Artificial Intelligence Text Classifier for Suicidality Prediction in Youth Crisis Text Line Users: Development and Validation Study.JMIR public health and surveillance · 2025Article
- Loneliness partially mediates the relation between substance use and suicidality in Veterans.Military psychology : the official journal of the Division of Military Psychology, American Psychological Association · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Theoretically-driven models of suicide have long guided suicidology; however, an approach employing machine learning models has recently emerged in the field. Some have suggested that machine learning models yield improved prediction as compared to theoretical approaches, but to date, this has not been investigated in a systematic manner. The present work directly compares widely researched theories of suicide (i.e., BioSocial, Biological, Ideation-to-Action, and Hopelessness Theories) to machine learning models, comparing the accuracy between the two differing approaches. We conducted literature searches using PubMed, PsycINFO, and Google Scholar, gathering effect sizes from theoretically-relevant constructs and machine learning models. Eligible studies were longitudinal research articles that predicted suicide ideation, attempts, or death published prior to May 1, 2020. 124 studies met inclusion criteria, corresponding to 330 effect sizes. Theoretically-driven models demonstrated suboptimal prediction of ideation (wOR = 2.87; 95% CI, 2.65-3.09; k = 87), attempts (wOR = 1.43; 95% CI, 1.34-1.51; k = 98), and death (wOR = 1.08; 95% CI, 1.01-1.15; k = 78). Generally, Ideation-to-Action (wOR = 2.41, 95% CI = 2.21-2.64, k = 60) outperformed Hopelessness (wOR = 1.83, 95% CI 1.71-1.96, k = 98), Biological (wOR = 1.04; 95% CI .97-1.11, k = 100), and BioSocial (wOR = 1.32, 95% CI 1.11-1.58, k = 6) theories. Machine learning provided superior prediction of ideation (wOR = 13.84; 95% CI, 11.95-16.03; k = 33), attempts (wOR = 99.01; 95% CI, 68.10-142.54; k = 27), and death (wOR = 17.29; 95% CI, 12.85-23.27; k = 7). Findings from our study indicated that across all theoretically-driven models, prediction of suicide-related outcomes was suboptimal. Notably, among theories of suicide, theories within the Ideation-to-Action framework provided the most accurate prediction of suicide-related outcomes. When compared to theoretically-driven models, machine learning models provided superior prediction of suicide ideation, attempts, and death.
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.