Using Machine Learning to Predict Suicide Attempts in Military Personnel.

Autor: Rozek DC; UCF RESTORES and Department of Psychology, University of Central Florida. Electronic address: david.rozek@ucf.edu., Andres WC; Boston College, Boston, MA., Smith NB; VA Northeast Program Evaluation Center, West Haven, CT, USA; Yale School of Medicine, New Haven, CT, USA., Leifker FR; University of Utah, Salt Lake City, UT, USA., Arne K; University of Utah, Salt Lake City, UT, USA., Jennings G; Varen Technologies., Dartnell N; MondoBrain., Bryan CJ; The Ohio State University Wexner Medical Center., Rudd MD; University of Memphis, Memphis, TN, USA.
Jazyk: angličtina
Zdroj: Psychiatry research [Psychiatry Res] 2020 Dec; Vol. 294, pp. 113515. Date of Electronic Publication: 2020 Oct 22.
DOI: 10.1016/j.psychres.2020.113515
Abstrakt: 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.
(Copyright © 2020. Published by Elsevier B.V.)
Databáze: MEDLINE
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