AIMC Topic: United States Department of Veterans Affairs
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Identifying Patient-Reported Outcome Measure Documentation in Veterans Health Administration Chiropractic Clinic Notes: Natural Language Processing Analysis.
JMIR medical informatics
Apr 2, 2025
BACKGROUND: The use of patient-reported outcome measures (PROMs) is an expected component of high-quality, measurement-based chiropractic care. The largest health care system offering integrated chiropractic care is the Veterans Health Administration...
Improving diagnosis-based quality measures: an application of machine learning to the prediction of substance use disorder among outpatients.
BMJ open quality
Mar 22, 2025
OBJECTIVE: Substance use disorder (SUD) is clinically under-detected and under-documented. We built and validated machine learning (ML) models to estimate SUD prevalence from electronic health record (EHR) data and to assess variation in facility-lev...
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
Mar 20, 2025
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...
Preprocessing of natural language process variables using a data-driven method improves the association with suicide risk in a large veterans affairs population.
Computers in biology and medicine
Mar 5, 2025
OBJECTIVE: Suicide risk assessment has historically relied heavily on clinical evaluations and patient self-reports. Natural language processing (NLP) of electronic health records (EHRs) provides an alternative approach for extracting risk predictors...
Young Adult
Adult
Male
United States Department of Defense
Middle Aged
Suicide, Completed
Risk Assessment
Predictive Learning Models
Case-Control Studies
Psychological Well-Being
Adolescent
Electronic Health Records
United States
Suicide Prevention
Female
Natural Language Processing
Aged
United States Department of Veterans Affairs
Aged, 80 and over
Veterans
Humans
A deep learning analysis for dual healthcare system users and risk of opioid use disorder.
Scientific reports
Jan 29, 2025
The opioid crisis has disproportionately affected U.S. veterans, leading the Veterans Health Administration to implement opioid prescribing guidelines. Veterans who receive care from both VA and non-VA providers-known as dual-system users-have an inc...
De Novo Natural Language Processing Algorithm Accurately Identifies Myxofibrosarcoma From Pathology Reports.
Clinical orthopaedics and related research
Oct 2, 2024
BACKGROUND: Available codes in the ICD-10 do not accurately reflect soft tissue sarcoma diagnoses, and this can result in an underrepresentation of soft tissue sarcoma in databases. The National VA Database provides a unique opportunity for soft tiss...
Using Natural Language Processing to develop risk-tier specific suicide prediction models for Veterans Affairs patients.
Journal of psychiatric research
Sep 24, 2024
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 prevention advances, efforts primarily target high-risk patients with documented suic...
Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans.
Psychiatry research
Jul 27, 2024
Measuring suicide risk fluctuation remains difficult, especially for high-suicide risk patients. Our study addressed this issue by leveraging Dynamic Topic Modeling, a natural language processing method that evaluates topic changes over time, to anal...
Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record.
ESC heart failure
Jun 14, 2024
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 patients identified using International Classification of Diseases (ICD) codes....
Improvements to PTSD quality metrics with natural language processing.
Journal of evaluation in clinical practice
May 24, 2021
RATIONALE AIMS AND OBJECTIVES: As quality measurement becomes increasingly reliant on the availability of structured electronic medical record (EMR) data, clinicians are asked to perform documentation using tools that facilitate data capture. These t...
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