A machine learning approach to identification of self-harm and suicidal ideation among military and police Veterans

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 signifi cantly diff erent in groups with and without SI. Th ese group-based methods are of limited value for identifying SI among individuals. Th e 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.