AIMC Topic: Veterans Health Services

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Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis.

JAMA network open Sep 1, 2020
IMPORTANCE: Deep learning, a family of machine learning models that use artificial neural networks, has achieved great success at predicting outcomes in nonmedical domains.
Neural Networks, Computer Prognosis Humans Liver Cirrhosis Area Under Curve Liver Neoplasms Carcinoma, Hepatocellular Veterans Health Services Chronic Disease Indicators Hepatitis C, Chronic Sustained Virologic Response Middle Aged Deep Learning Electronic Health Records Risk Assessment Incidence Female Male
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Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.

Circulation. Cardiovascular quality and outcomes Oct 15, 2019
BACKGROUND: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitorin...
Predictive Value of Tests Signal Processing, Computer-Assisted Stroke Atrial Fibrillation Logistic Models United States Time Factors Prognosis Retrospective Studies Aged Neural Networks, Computer Electronic Health Records Risk Factors Telemetry Risk Assessment Aged, 80 and over Female Diagnosis, Computer-Assisted Humans Machine Learning Veterans Health Services Male Proof of Concept Study Middle Aged Administrative Claims, Healthcare
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Does machine learning improve prediction of VA primary care reliance?

The American journal of managed care Jan 1, 2020
OBJECTIVES: The Veterans Affairs (VA) Health Care System is among the largest integrated health systems in the United States. Many VA enrollees are dual users of Medicare, and little research has examined methods to most accurately predict which vete...
United States Department of Veterans Affairs Patient Acceptance of Health Care Medicare Veterans Health Services Middle Aged Primary Health Care Logistic Models United States Aged, 80 and over Male Aged Forecasting Female Humans Machine Learning
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