Citation

Colic S, He JC, Richardson JD, Cyr KS, Reilly JP, Hasey GM. J. Mil. Veteran Fam. Health 2022; 8(1): 56-67.

Copyright

(Copyright © 2022, University of Toronto Press)

DOI

10.3138/JMVFH-2021-0035

PMID

unavailable

Abstract

INTRODUCTION: Combat Veterans are at increased risk for suicidal ideation (SI). Many who die by suicide deny having SI, so alternative approaches to asking about suicide are needed. Current statistical approaches test whether a hypothesized SI predictor variable is significantly different in groups with and without SI. These group-based methods are of limited value for identifying SI among individuals. The objective of this study was to test the utility and feasibility of machine learning (ML) analysis of the kind of data that could be easily collected in an operational stress injury clinic in order to offer new insights into the detection of SI.

METHODS: ML algorithms to detect self-harm and SI (SHSI) were trained using 192 variables from questionnaires administered to 738 Veterans and serving members. An autoencoder was used to impute missing data to maximize training sample size.

RESULTS: The ML algorithms detected SHSI with an accuracy of 75.3% (area under the receiver operating characteristic curve = 82.7%). Of the 10 items identified, none asked about suicide.

DISCUSSION: ML methods can detect patterns predictive of SHSI in large data sets and could aid in early intervention and, ultimately, suicide prevention for individuals. © 2022 University of Toronto Press. All Rights Reserved.

Language: en

Keywords

suicide; PTSD; self-harm; suicidal ideation; Veterans; posttraumatic stress disorder; combat; SI; machine learning; Canadian Armed Forces; CAF; RCMP; Royal Canadian Mounted Police