Improving explainability of post-separation suicide attempt prediction models for transitioning service members: insights from the Army Study to Assess Risk and Resilience in Servicemembers - Longitudinal Study.

ABSTRACT: Risk of U.S. Army soldier suicide-related behaviors increases substantially after separation from service. As universal prevention programs have been unable to resolve this problem, a previously reported machine learning model was developed using pre-separation predictors to target high-risk transitioning service members (TSMs) for more intensive interventions. This model is currently being used in a demonstration project. The model is limited, though, in two ways. First, the model was developed and trained in a relatively small cross-validation sample (n = 4044) and would likely be improved if a larger sample was available. Second, the model provides no guidance on subtyping high-risk TSMs. This report presents results of an attempt to refine the model to address these limitations by re-e

SUBMITTER: Edwards ER 

PROVIDER: S-EPMC11782477 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature