Gender Differences in Machine Learning Models of Trauma and Suicidal Ideation in Veterans of the Iraq and Afghanistan Wars

Abstract

Suicide rates among recent veterans have led to interest in risk identification. Evidence of gender-and trauma-specific predictors of suicidal ideation necessitates the use of advanced computational methods capable of elucidating these important and complex associations. In this study, we used machine learning to examine gender-specific associations between predeployment and military factors, traumatic deployment experiences, and psychopathology and suicidal ideation (SI) in a national sample of veterans deployed during the Iraq and Afghanistan conflicts (n = 2,244). Classification, regression tree analyses, and random forests were used to identify associations with SI and determine their classification accuracy. Findings converged on several associations for men that included depression, posttraumatic stress disorder (PTSD), and somatic complaints. Sexual harassment during deployment emerged as a key factor that interacted with PTSD and depression and demonstrated a stronger association with SI among women. Classification accuracy for SI presence or absence was good based on the receiver operating characteristic area under the curve, men = .91, women = .92. The risk for SI was classifiable with good accuracy, with associations that varied by gender. The use of machine learning analyses allowed for the discovery of rich, nuanced results that should be replicated in other samples and may eventually be a basis for the development of gender-specific actuarial tools to assess SI risk among veterans.

Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations

“…Regarding generalizability, reports reflected a transdiagnostic focus, and primarily assessed adult participants or patient records. A smaller number of reports examined high-risk, pediatric or geriatric samples, as well as military veterans [ 15 , 26 , 33 , 34 , 53 , 72 , 97 , 112 ]. These highlight areas of elevated need, and align with prioritized strategies and nationally-directed initiatives for technology innovation in suicide prevention [ 5 , 6 , 130 ].…”

“…Such surveys, however, frequently used a single item assessment of suicidal behaviors, which may misclassify risk [ 132 , 133 ]. In general, suicide outcomes were variably defined, and validated symptom instruments varied significantly across reports [ 26 , 56 , 57 , 59 , 72 , 78 , 86 , 95 , 102 , 103 ]. This aligns with calls for increased uniformity in the assessment of suicidal behaviors to enhance research comparisons and improve surveillance [ 31 ].…”

Machine learning multivariate pattern analysis predicts classification of posttraumatic stress disorder and its dissociative subtype: a multimodal neuroimaging approach

“…Critically, regions identified as being heavily weighted for group classification were also identified as significantly different between groups when conducting a whole-brain corrected mass-univariate analysis. Recently, a growing number of studies have applied machine learning methods to neuroimaging data to predict and classify psychiatric illness (Bleich-cohen et al ., 2014; Mikolas et al ., 2016; Rive et al ., 2016), including PTSD, typically based on the predictive value of current PTSD symptoms, cortisol levels, and pre-trauma risk factors (Gong et al ., 2014; Karstoft et al ., 2015; Liu et al ., 2015; Omurca and Ekinci, 2015; Galatzer-Levy et al ., 2017; Gradus et al ., 2017; Jin et al ., 2017; Saxe et al ., 2017). Resting-state activation has also been utilized to predict PTSD symptom presentation through machine learning (Gong et al ., 2014; Liu et al ., 2015), where functional connectivity maps differentiate PTSD patients from controls (Liu et al ., 2015; Jin et al ., 2017).…”

The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors

“…Beyond patients with low mood, prediction models have successfully classified suicide risk in patients with other psychiatric (Gradus et al, 2017; Hettige et al, 2017) and medical (Kalinin and Polyanskiy, 2005) diagnoses, and even among seemingly healthy demographics such as students (Mortier et al, 2018), prisoners (Bonner and Rich, 1990) and soldiers (Kessler et al, 2015, 2017). Soldiers, in particular, have been considered a high-risk group with an identified need for targeted prediction models.…”