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A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis

Truvace record
TRV-2026-2812raw data (JSON)
Version
v1Published
First published
Evidence type
Systematic review or meta-analysis
Sources
1 sourcePeer-reviewed
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fe8169ac30ec…check it

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…

The gain

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).

What this doesn’t fix

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.

Sources

1 on file
  1. PLOS ONEdoi:10.1371/journal.pone.0249833
    Peer-reviewed2021-04-12

What the review found when it combined the studies

These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.

  • Prediction of combined suicidal thoughts and behaviours by Ideation-to-Action theory

    Weighted odds ratio 2.41 (95% CI 2.21 to 2.64)

    124 studies · search to May 1, 2020

    The review’s own words
    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.

  • Prediction of suicide attempts by theory-driven models

    Weighted odds ratio 1.43 (95% CI 1.34 to 1.51)

    124 studies · search to May 1, 2020

    The review’s own words
    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).

  • Prediction of combined suicidal thoughts and behaviours by machine learning models

    Weighted odds ratio 18.09 (95% CI 15.98 to 20.47)

    124 studies · search to May 1, 2020

    The review’s own words
    Effect sizes related to machine learning indicated that on the whole machine learning approaches provided superior prediction of all suicide-related outcomes ( w OR = 18.09, 95% CI 15.98–20.47 k = 66)

  • Prediction of suicide ideation by machine learning models

    Weighted odds ratio 13.84 (95% CI 11.95 to 16.03)

    124 studies · search to May 1, 2020

    The review’s own words
    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).

  • Prediction of suicide attempts by machine learning models

    Weighted odds ratio 99.01 (95% CI 68.10 to 142.54)

    124 studies · search to May 1, 2020

    The review’s own words
    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).

  • Prediction of combined suicidal thoughts and behaviours by Hopelessness theory

    Weighted odds ratio 1.83 (95% CI 1.71 to 1.96)

    124 studies · search to May 1, 2020

    The review’s own words
    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.

  • Prediction of suicide death by theory-driven models

    Weighted odds ratio 1.08 (95% CI 1.01 to 1.15)

    124 studies · search to May 1, 2020

    The review’s own words
    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).

  • Prediction of suicide death by machine learning models

    Weighted odds ratio 17.29 (95% CI 12.85 to 23.27)

    124 studies · search to May 1, 2020

    The review’s own words
    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).

  • Prediction of suicide ideation by theory-driven models

    Weighted odds ratio 2.87 (95% CI 2.65 to 3.09)

    124 studies · search to May 1, 2020

    The review’s own words
    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).

  • Prediction of combined suicidal thoughts and behaviours by theory-driven models

    Weighted odds ratio 1.74 (95% CI 1.66 to 1.82)

    124 studies · search to May 1, 2020

    The review’s own words
    all effect sizes related to theoretically-driven models predicted combined STBs ( w OR = 1.74; 95% CI, 1.66–1.82; k = 264)

Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results

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v1

Truvace Impact Record TRV-2026-2812, v1: “A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis.” Truvace, 2026-11-12. /record/TRV-2026-2812 (accessed on the day you copy it). sha256 fe8169ac30ec6fdc…

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