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TRUVACE RECORD VERSION
record: TRV-2026-2812
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:27:42.167520Z
status: published
lens: g_space
sector: health
headline: A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis
dek: 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…
gain_title: In a meta-analysis of 124 studies (330 effect sizes), machine learning models predicted suicide attempts with a weighted odds ratio of 99.01, against 1.43 for theory-driven models, and also beat them on ideation (13.84 vs 2.87) and death (17.29 vs 1.08).
problem_title: (none)
trace_subject: (none)
gain_reading: In a meta-analysis of 124 studies (330 effect sizes), machine learning models predicted suicide attempts with a weighted odds ratio of 99.01, against 1.43 for theory-driven models, and also beat them on ideation (13.84 vs 2.87) and death (17.29 vs 1.08).
gain_evidence: attempts (wOR = 99.01; 95% CI, 68.10-142.54; k = 27) | machine learning models provided superior prediction of suicide ideation, attempts, and death
problem_reading: (none)
problem_evidence: (none)
quick_read: A meta-analysis of 124 longitudinal and other studies, covering 330 effect sizes published before May 2020, compared how well four leading theories of suicide and machine learning models predict suicidal ideation, attempts and death. Machine learning models produced much higher weighted odds ratios: 13.84 for ideation, 99.01 for attempts and 17.29 for death.

The theory-driven models did poorly by comparison. Their weighted odds ratios were 2.87 for ideation, 1.43 for attempts and 1.08 for death, which the authors describe as suboptimal and close to chance. Among the theories, those in the Ideation-to-Action framework did best (2.41), ahead of Hopelessness (1.83), BioSocial (1.32) and Biological (1.04).

The gap comes with caveats. Few machine learning studies reported enough data to be pooled (66 effect sizes), heterogeneity was very high, and small-sample publication bias was found in both groups. Machine learning accuracy did not improve over publication years, and very high odds ratios were the exception rather than the rule.
limitation: Few machine learning studies supplied enough data to pool, heterogeneity was very high, and small-sample publication bias was found in both approaches, so pooled effect sizes should be read with caution.
tag: Evidence-backed gain
key_points: The meta-analysis searched PubMed, PsycINFO and Google Scholar; 124 of 596 publications met inclusion criteria, yielding 330 effect sizes. | Combined theory-driven models had a weighted odds ratio of 1.74, while machine learning models reached 18.09 across 66 effect sizes. | Machine learning predicted attempts best (weighted odds ratio 99.01, 27 effect sizes), then death (17.29, 7 effect sizes) and ideation (13.84, 33 effect sizes). | Biological theories predicted outcomes no better than chance (1.04), while Ideation-to-Action theories scored highest among theories (2.41), driven mainly by ideation. | Longitudinal machine learning effect sizes (weighted odds ratio 35.82) were significantly stronger than cross-sectional ones (16.45). | Machine learning accuracy did not improve across publication years (p = 0.17), while theory-driven models improved slightly (b = .023, p < .001). | After correcting for publication bias with trim and fill, theory-driven models fell to 1.05 and machine learning to 17.18.
rundown: The authors included English-language studies; theory-driven studies had to include at least one longitudinal analysis, while machine learning studies could be cross-sectional, longitudinal or experimental because the literature was thin. Effect sizes were converted to odds ratios, pooled with random-effects models, and two coders extracted data with an intraclass correlation of .978. Of 26 additional machine learning manuscripts that lacked data, only 5 authors supplied it.

Among theories, Biological studies made up 37.8% of effect sizes, Ideation-to-Action 37.1%, Hopelessness 22.7% and BioSocial 2.3%. Ideation-to-Action predicted ideation (3.05) about twice as well as attempts (1.52), and Hopelessness predicted ideation (3.38) better than attempts (1.55) or death (1.34). The authors note that theories were built to explain causes, not to optimise prediction.

The authors caution that machine learning models were usually trained and tested on very similar samples, so a model built on adolescents may not work for veterans. They also note that high AUC values can hide poor positive predictive value, and they call for studies to report true and false positives and negatives. They add that machine learning models generally do not explain causes, which gives them less clinical use for treatment targets, and they raise privacy, consent and fairness concerns about deploying them.
sources:
- peer_reviewed | PLOS ONE | https://doi.org/10.1371/journal.pone.0249833 | 2021-04-12
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