AIMC Topic: United States Department of Veterans Affairs
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Classification of radiology reports for falls in an HIV study cohort.
Journal of the American Medical Informatics Association : JAMIA
Nov 13, 2015
OBJECTIVE: To identify patients in a human immunodeficiency virus (HIV) study cohort who have fallen by applying supervised machine learning methods to radiology reports of the cohort.
Scaling Out and Evaluation of OBSecAn, an Automated Section Annotator for Semi-Structured Clinical Documents, on a Large VA Clinical Corpus.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Nov 5, 2015
"Identifying and labeling" (annotating) sections improves the effectiveness of extracting information stored in the free text of clinical documents. OBSecAn, an automated ontology-based section annotator, was developed to identify and label sections ...
Evaluation of PHI Hunter in Natural Language Processing Research.
Perspectives in health information management
Jan 1, 2015
OBJECTIVES: We introduce and evaluate a new, easily accessible tool using a common statistical analysis and business analytics software suite, SAS, which can be programmed to remove specific protected health information (PHI) from a text document. Re...
The use of natural language processing of infusion notes to identify outpatient infusions.
Pharmacoepidemiology and drug safety
Nov 17, 2014
PURPOSE: Outpatient infusions are commonly missing in Veterans Health Affairs (VHA) pharmacy dispensing data sets. Currently, Healthcare Common Procedure Coding System (HCPCS) codes are used to identify outpatient infusions, but concerns exist if the...
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...
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...
Comparison of six natural language processing approaches to assessing firearm access in Veterans Health Administration electronic health records.
Journal of the American Medical Informatics Association : JAMIA
Jan 1, 2025
OBJECTIVE: Access to firearms is associated with increased suicide risk. Our aim was to develop a natural language processing approach to characterizing firearm access in clinical records.
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...
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...
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