Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation-Reference-Cited by-同舟云学术

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Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation

Published:2020-01-17 Issue:1 Volume:3 Page:e1919396
ISSN:2574-3805
Container-title:JAMA Network Open
language:en
Short-container-title:JAMA Netw Open

Author:

Tiwari Premanand¹,Colborn Kathryn L.²,Smith Derek E.³,Xing Fuyong²,Ghosh Debashis²,Rosenberg Michael A.¹⁴

Affiliation:

1. Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora

2. Colorado School of Public Health, Department of Biostatics and Informatics, University of Colorado Denver, Aurora

3. Children’s Hospital Colorado, Cancer Center Biostatistics Core, Department of Pediatrics, University of Colorado, Aurora

4. Division of Cardiology and Cardiac Electrophysiology, University of Colorado School of Medicine, Aurora

Publisher

American Medical Association (AMA)

Subject

General Medicine

Link

https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2758859/tiwari_2020_oi_190726.pdf

Reference75 articles.

1. Trends in the prevalence and management of atrial fibrillation in general practice in England and Wales, 1994-1998: analysis of data from the general practice research database.;Majeed;Heart,2001

2. Heart disease and stroke statistics, 2017 update: a report from the American Heart Association.;Benjamin;Circulation,2017

3. Impact of atrial fibrillation on the risk of death: the Framingham Heart Study.;Benjamin;Circulation,1998

4. Independent risk factors for atrial fibrillation in a population-based cohort: the Framingham Heart Study.;Benjamin;JAMA,1994

5. The effect of low-dose warfarin on the risk of stroke in patients with nonrheumatic atrial fibrillation.;Singer;N Engl J Med,1990

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