AIMC Topic: Veterans

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Staying Relevant in the Digital Age: Exploring the Evolving Frontier of Telehealth for Mental Health in the Military Health System and Veterans Health Administration.

Current psychiatry reports
PURPOSE OF REVIEW: This review examines recent evidence for the effectiveness of telehealth in treating Post-Traumatic Stress Disorder (PTSD), Major Depressive Disorder (MDD), alcohol use disorder, and insomnia in military veterans and active-duty se...

When does machine learning outperform clinicians? A comparison of prediction accuracy for PTSD treatment outcomes.

Psychological medicine
BACKGROUND: Machine learning (ML) models show promise in predicting post-traumatic stress disorder (PTSD) treatment outcomes, but it is unknown how their predictions compare to those of clinicians. This study directly compared the accuracy of clinici...

Using Digital Phenotypes to Identify Individuals With Alexithymia in Posttraumatic Stress Disorder: Cross-Sectional Study.

JMIR mental health
BACKGROUND: Alexithymia, defined as difficulty identifying and describing one's emotions, has been identified as a transdiagnostic emotional process that impacts the course, severity, and treatment outcomes of psychiatric conditions such as posttraum...

Predictors of adjustment to life after service among Canadian military veterans.

Scientific reports
The transition out of military service and into civilian life represents a considerable challenge for many military veterans. In this study we used mixture growth modeling and random forest analysis to examine predictors of adjustment to civilian lif...

Use of machine learning for early prediction of short-term mortality in veterans with metabolic dysfunction-associated steatotic liver disease.

PloS one
BACKGROUND: Metabolic dysfunction associated steatotic liver disease (MASLD) is a leading cause of chronic liver disease worldwide and affects >25% in the United States population. We hypothesized that clinical features present in electronic health r...

Clinical Information Extraction From Notes of Veterans With Lymphoid Malignancies: Natural Language Processing Study.

JMIR medical informatics
BACKGROUND: Clinical natural language processing (cNLP) techniques are commonly developed and used to extract information from clinical notes to facilitate clinical decision-making and research. However, they are less established for rare diseases su...

Misleading Results in Posttraumatic Stress Disorder Predictive Models Using Electronic Health Record Data: Algorithm Validation Study.

Journal of medical Internet research
BACKGROUND: Electronic health record (EHR) data are increasingly used in predictive models of posttraumatic stress disorder (PTSD), but it is unknown how multivariable prediction of an EHR-based diagnosis might differ from prediction of a more rigoro...

Sleep disturbances and PTSD: identifying baseline predictors of insomnia response in an intensive treatment programme.

European journal of psychotraumatology
This study examined whether baseline demographic and clinical variables could predict clinically significant reductions in insomnia symptoms among veterans receiving a 2-week Cognitive Processing Therapy (CPT)-based intensive PTSD treatment programm...

Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data.

Journal of psychopathology and clinical science
Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use an...