Candidate genes from an FDA-approved algorithm fail to predict opioid use disorder risk in over 450,000 Veterans

Abstract: Importance: Recently, the Food and Drug Administration gave pre-marketing approval to algorithm based on its purported ability to identify genetic risk for opioid use …

Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia

Abstract: STUDY OBJECTIVES: The standard of care for military personnel with insomnia is cognitive behavioral therapy for insomnia (CBT-I). However, only a minority seeking …

Predicting homelessness among transitioning U.S. Army soldiers

Abstract: INTRODUCTION: This study develops a practical method to triage Army transitioning service members (TSMs) at highest risk of homelessness to target a preventive …

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 …

A prognostic index to predict symptom and functional outcomes of a coached, web-based intervention for trauma-exposed veterans

Abstract: Researchers at the Department of Veterans Affairs (VA) have studied interventions for posttraumatic stress disorder and co-occurring conditions in both traditional and …

Quantifying healthy aging in older Veterans using computational audio analysis

Abstract: BACKGROUND: Researchers are increasingly interested in better methods for assessing the pace of aging in older adults, including vocal analysis. The present study sought …

Diagnosing noise-induced hearing loss sustained during military service using deep neural networks

Abstract: The diagnosis of noise-induced hearing loss (NIHL) is based on three requirements: a history of exposure to noise with the potential to cause hearing loss; the absence of …

Framework for accurate classification of self-reported stress from multisession functional MRI data of veterans with posttraumatic stress

Abstract: Background: Posttraumatic stress disorder (PTSD) is a significant burden among combat Veterans returning from the wars in Iraq and Afghanistan. While empirically …

Development of a model to predict antidepressant treatment response for depression among Veterans

Abstract: Background: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the …

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 …

Forecasting military mental health in a complete sample of Danish military personnel deployed between 1992-2013

Abstract: Objective: Mental health problems (MHP) are a relatively common consequence of deployment to war zones. Early identification of those at risk of post-deployment MHP …

The Development of the Military Service Identification Tool: Identifying Military Veterans in a Clinical Research Database Using Natural Language Processing and Machine Learning

Abstract: BACKGROUND:Electronic health care records (EHRs) are a rich source of health-related information, with potential for secondary research use. In the United Kingdom, there …