Development of a prediction model for 30-day COVID-19 hospitalization and death in a national cohort of Veterans Health Administration patients, March 2022-April 2023

Abstract: Objective: The epidemiology of COVID-19 has substantially changed since its emergence given the availability of effective vaccines, circulation of different viral …

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 …

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts

Abstract: Background: Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex …

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 …

Initial evaluation of a personalized advantage index to determine which individuals may benefit from mindfulness-based cognitive therapy for suicide prevention

Abstract: Objective: Develop and evaluate a treatment matching algorithm to predict differential treatment response to Mindfulness-Based Cognitive Therapy for suicide prevention …

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 …

Detecting suicide risk among U.S. Service members and Veterans: A deep learning approach using social media data

Abstract: Background: Military Servicemembers and Veterans are at elevated risk for suicide, but rarely self-identify to their leaders or clinicians regarding their experience of …

Developing a nationwide registry of UK Veterans seeking help from sector charities-a machine learning approach to stratification

Abstract: The assistance to veterans in the UK is provided by the National Health Service and over 1800 military charities. These charities count services using different …

The application of machine learning and causal inference to improve suicide outcomes among U.S. Veterans: A focus on clinic characteristics

Abstract: Suicide is the tenth leading cause of death among the general U.S. population and the second leading cause of death among those under the age of 45. Veterans are at a …

Organizational and patient factors associated with positive primary care experiences for Veterans with current or recent homelessness

Objective: To identify organizational service features associated with positive patient ratings of primary care within primary care clinics tailored to accommodate persons with …

Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among U.S. military Veterans treated with buprenorphine for opioid use disorder

Abstract: Background: Buprenorphine for opioid use disorder (B-MOUD) is essential to improving patient outcomes; however, retention is essential. Objective: To develop and validate …

Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record

Abstract: Aims: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF …