Predicting firearm suicide among US Army Veterans transitioning from active service

Abstract: US veterans are significantly more likely than civilians to die by suicide. Machine-learning models have been developed to target high-risk transitioning service members …

Predicting suicide death among Veterans after psychiatric hospitalization using transformer based models with social determinants and NLP

Abstract: Predictions of suicide death of patients discharged from psychiatric hospitals (PDPH) can guide intervention efforts including intensive post-discharge case management …

Predicting suicides among US Army soldiers after leaving active service

Abstract: Importance: The suicide rate of military servicemembers increases sharply after returning to civilian life. Identifying high-risk servicemembers before they leave service …

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 …

High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning

Abstract: We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained …