Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes-Reference-Cited by-同舟云学术

Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes

Published:2020-01-10 Issue:1 Volume:3 Page:e1918962
ISSN:2574-3805
Container-title:JAMA Network Open
language:en
Short-container-title:JAMA Netw Open

Author:

Desai Rishi J.¹,Wang Shirley V.¹,Vaduganathan Muthiah²,Evers Thomas³,Schneeweiss Sebastian¹

Affiliation:

1. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts

2. Heart and Vascular Center, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts

3. Market Access, Bayer AG, Wuppertal, Germany

Publisher

American Medical Association (AMA)

Subject

General Medicine

Link

https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2758475/desai_2020_oi_190713.pdf

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2. Heart disease and stroke statistics—2018 update: a report from the American Heart Association.;Benjamin;Circulation,2018

3. Heart failure–associated hospitalizations in the United States.;Blecker;J Am Coll Cardiol,2013