• Author: Joshua J. Levy
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Investigating the differential impact of psychosocial factors by patient characteristics and demographics on Veteran suicide risk through machine learning extraction of cross-modal interactions

Accurate prediction of suicide risk is crucial for identifying patients with elevated risk burden, helping ensure these patients receive targeted care. The US Department of Veteran …

Preprocessing of natural language process variables using a data-driven method improves the association with suicide risk in a large Veterans affairs population

Abstract: Objective: Suicide risk assessment has historically relied heavily on clinical evaluations and patient self-reports. Natural language processing (NLP) of electronic …

Evaluating evidence-based psychotherapy utilization patterns among suicide-risk-stratified Veterans diagnosed with posttraumatic stress disorder

Abstract: Posttraumatic stress disorder (PTSD) is a prevalent psychiatric condition, particularly among US Veterans. PTSD-diagnosed patients are more likely to experience suicidal …

Using natural language processing to develop risk-tier specific suicide prediction models for Veterans Affairs patients

Abstract: Suicide is a leading cause of death. Suicide rates are particularly elevated among Department of Veterans Affairs (VA) patients. While VA has made impactful suicide …

Characterizing Veteran suicide decedents that were not classified as high-suicide-risk

Abstract: Background: Although the Department of Veterans Affairs (VA) has made important suicide prevention advances, efforts primarily target high-risk patients with documented …

Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans

Abstract: Measuring suicide risk fluctuation remains difficult, especially for high-suicide risk patients. Our study addressed this issue by leveraging Dynamic Topic Modeling, a …