AIMC Topic: Veterans
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Combining Machine Learning and Comparative Effectiveness Methodology to Study Primary Care Pharmacotherapy Pathways for Veterans With Depression.
Medical care
Jun 1, 2025
OBJECTIVES: To demonstrate an innovative method combining machine learning with comparative effectiveness research techniques and to investigate a hitherto unstudied question about the effectiveness of common prescribing patterns.
Computing 3-Step Theory of Suicide Factor Scores From Veterans Health Administration Clinical Progress Notes.
Suicide & life-threatening behavior
Feb 1, 2025
BACKGROUND: Literature on how to translate information extracted from clinical progress notes into numeric scores for 3-step theory of suicide (3ST) factors is nonexistent. We determined which scoring option would best discriminate between patients w...
Developing a nationwide registry of UK veterans seeking help from sector charities-a machine learning approach to stratification.
European journal of public health
Feb 1, 2025
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 definitions and reporting systems, so to date a national registry of service usage does n...
Investigating the Differential Impact of Psychosocial Factors by Patient Characteristics and Demographics on Veteran Suicide Risk Through Machine Learning Extraction of Cross-Modal Interactions.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2025
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 Affairs' suicide prediction model primarily leverages structured elec...
Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.
JCO clinical cancer informatics
Jul 1, 2024
PURPOSE: Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to develop and validate a natural language processing (NLP) algorithm to extract oncologists' documenta...
Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation.
JAMA cardiology
Dec 1, 2023
IMPORTANCE: Early detection of atrial fibrillation (AF) may help prevent adverse cardiovascular events such as stroke. Deep learning applied to electrocardiograms (ECGs) has been successfully used for early identification of several cardiovascular di...
Usability and Acceptability of Social Robot Pets Among Community-Dwelling Veterans Living With Dementia and Their Caregivers.
American journal of Alzheimer's disease and other dementias
Jan 1, 2023
Social robot pets promote engagement and psychosocial well-being among older adults, yet little is known about their use among community-dwelling Veterans living with dementia. This programmatic evaluation used a within subjects, pre-post design to e...
Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs.
BMJ health & care informatics
Jun 1, 2021
Using administrative data on all Veterans who enter Department of Veterans Affairs (VA) medical centres throughout the USA, this paper uses artificial intelligence (AI) to predict mortality rates for patients with COVID-19 between March and August 20...
Natural Language Processing of Serum Protein Electrophoresis Reports in the Veterans Affairs Health Care System.
JCO clinical cancer informatics
Aug 1, 2020
PURPOSE: Serum protein electrophoresis (SPEP) is a clinical tool used to screen for monoclonal gammopathy, thus it is a critical tool in the evaluation of patients with multiple myeloma. However, SPEP laboratory results are usually returned as short ...
Measuring Exposure to Incarceration Using the Electronic Health Record.
Medical care
Jun 1, 2019
BACKGROUND: Electronic health records (EHRs) are a rich source of health information; however social determinants of health, including incarceration, and how they impact health and health care disparities can be hard to extract.
Prisoners
Cohort Studies
United States Department of Veterans Affairs
Adult
United States
Veterans
Sensitivity and Specificity
Natural Language Processing
Medicare
Male
Administrative Claims, Healthcare
Humans
Self Report
Middle Aged
Electronic Health Records
Ethnicity
Female
Information Storage and Retrieval
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