AIMC Topic: Veterans

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Incorporation of quantitative imaging data using artificial intelligence improves risk prediction in veterans with liver disease.

Hepatology (Baltimore, Md.)
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
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 ...

Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach.

Scientific reports
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
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
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 ...

Detection of probable dementia cases in undiagnosed patients using structured and unstructured electronic health records.

BMC medical informatics and decision making
BACKGROUND: Dementia is underdiagnosed in both the general population and among Veterans. This underdiagnosis decreases quality of life, reduces opportunities for interventions, and increases health-care costs. New approaches are therefore necessary ...

Machine learning models to predict disease progression among veterans with hepatitis C virus.

PloS one
BACKGROUND: Machine learning (ML) algorithms provide effective ways to build prediction models using longitudinal information given their capacity to incorporate numerous predictor variables without compromising the accuracy of the risk prediction. C...

Applying Machine Learning to Linked Administrative and Clinical Data to Enhance the Detection of Homelessness among Vulnerable Veterans.

AMIA ... Annual Symposium proceedings. AMIA Symposium
U.S. military veterans who were discharged from service for misconduct are at high risk for homelessness. Stratifying homelessness risk based on both military service factors and clinical characteristics could facilitate targeted provision of prevent...