AIMC Topic: Veterans
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Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder.
Journal of addictive diseases
Jun 30, 2024
BACKGROUND: Buprenorphine for opioid use disorder (B-MOUD) is essential to improving patient outcomes; however, retention is essential.
A machine learning personalized treatment rule to optimize assignment to psychotherapies for grief among veterans.
Journal of affective disorders
May 7, 2024
BACKGROUND: Complex grief patterns are associated with significant suffering, functional impairments, health and mental health problems, and increased healthcare use. This burden may be even more pronounced among veterans. Behavioral Activation and T...
Sexual and Gender Minority Status and Suicide Mortality: An Explainable Artificial Intelligence Analysis.
International journal of public health
May 6, 2024
Suicide risk is elevated in lesbian, gay, bisexual, and transgender (LGBT) individuals. Limited data on LGBT status in healthcare systems hinder our understanding of this risk. This study used natural language processing to extract LGBT status and a...
Incorporation of quantitative imaging data using artificial intelligence improves risk prediction in veterans with liver disease.
Hepatology (Baltimore, Md.)
Dec 29, 2023
BACKGROUND AND AIMS: Utilization of electronic health records data to derive predictive indexes such as the electronic Child-Turcotte-Pugh (eCTP) Score can have significant utility in health care delivery. Within the records, CT scans contain phenoty...
Identifying depression in the United States veterans using deep learning algorithms, NHANES 2005-2018.
BMC psychiatry
Aug 23, 2023
BACKGROUND: Depression is a common mental health problem among veterans, with high mortality. Despite the numerous conducted investigations, the prediction and identification of risk factors for depression are still severely limited. This study used ...
Invited Commentary: Challenging Current Notions in Foregut Surgery: A Veterans Affairs Surgical Quality Improvement Program Database Analysis of the Robotic and Laparoscopic Approach to Benign Foregut Surgery.
Journal of the American College of Surgeons
Apr 8, 2022
Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach.
Scientific reports
Apr 14, 2021
Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed network graph to electronic health record (EHR) data and characterized comorbidities in a cohort of healt...
A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data.
PloS one
Feb 19, 2021
BACKGROUND: Identifying individuals at risk for future hospitalization or death has been a major priority of population health management strategies. High-risk individuals are a heterogeneous group, and existing studies describing heterogeneity in hi...
Regional Variations in Documentation of Sexual Trauma Concepts in Electronic Medical Records in the United States Veterans Health Administration.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Mar 4, 2020
Experiences of sexual trauma are associated with adverse patient and health system outcomes, but are not systematically documented in electronic health records (EHR). To describe variations in how sexual trauma is documented in the Veterans Health ...
Identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the HI-FIVES randomized clinical trial.
Trials
Feb 14, 2020
BACKGROUND: Informal caregivers report substantial burden and depressive symptoms which predict higher rates of patient institutionalization. While caregiver education interventions may reduce caregiver distress and decrease the use of long-term inst...
Family
Aged, 80 and over
Caregivers
Male
Middle Aged
Institutionalization
Quality of Life
Long-Term Care
Humans
Follow-Up Studies
Depression
Machine Learning
Age Factors
Stress, Psychological
Randomized Controlled Trials as Topic
Female
Cognitive Dysfunction
Data Interpretation, Statistical
Aged
Treatment Outcome
Veterans
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