Using Machine Learning to Predict Suicide Attempts in Military Personnel

David C. Rozek, William C. Andres, Noelle B. Smith, Feea R. Leifker, Kim Arne, Greg Jennings, Nate Dartnell, Craig J. Bryan, M. David Rudd

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

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.

Original languageEnglish (US)
Article number113515
JournalPsychiatry Research
Volume294
DOIs
StatePublished - Dec 2020
Externally publishedYes

Keywords

  • Army
  • machine learning
  • military
  • prediction
  • Suicide

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Biological Psychiatry

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