A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis
Open Access
- 12 April 2021
- journal article
- research article
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 16 (4), e0249833
- https://doi.org/10.1371/journal.pone.0249833
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.Funding Information
- National Institute of Mental Health (T32MH093311)
This publication has 39 references indexed in Scilit:
- Predictors of suicide attempts in patients with borderline personality disorder over 16 years of prospective follow-upPsychological Medicine, 2012
- Risk Factors for Suicidal Ideation Among African American and European American College WomenPsychology of Women Quarterly, 2012
- Towards an Integrated Motivational–Volitional Model of Suicidal BehaviourPublished by Wiley ,2011
- Balancing Access to Health Data and Privacy: A Review of the Issues and Approaches for the FutureHealth Services Research, 2010
- The interpersonal theory of suicide.Psychological Review, 2010
- A prospective study of the association of cerebrospinal fluid monoamine metabolite levels with lethality of suicide attempts in patients with bipolar disorderBipolar Disorders, 2006
- The dexamethasone suppression test as a predictor of suicidal behavior in unipolar depressionJournal of Affective Disorders, 2004
- Hopelessness as a Predictor of Eventual SuicideAnnals of the New York Academy of Sciences, 1986
- Suicide in AlcoholismArchives of General Psychiatry, 1984