Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram-Reference-Cited by-同舟云学术

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Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram

Published:2019-05-01 Issue:5 Volume:4 Page:428
ISSN:2380-6583
Container-title:JAMA Cardiology
language:en
Short-container-title:JAMA Cardiol

Author:

Galloway Conner D.¹,Valys Alexander V.¹,Shreibati Jacqueline B.¹,Treiman Daniel L.¹,Petterson Frank L.¹,Gundotra Vivek P.¹,Albert David E.¹,Attia Zachi I.²,Carter Rickey E.²³,Asirvatham Samuel J.²,Ackerman Michael J.²,Noseworthy Peter A.²,Dillon John J.⁴,Friedman Paul A.²

Affiliation:

1. AliveCor Inc, Mountain View, California

2. Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota

3. Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida

4. Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota

Publisher

American Medical Association (AMA)

Subject

Cardiology and Cardiovascular Medicine

Link

http:///jamacardiology/article-pdf/4/5/428/17556075/jamacardiology_galloway_2019_oi_190012.pdf

Reference35 articles.

1. The clinical and economic burden of hyperkalemia on medicare and commercial payers.;Fitch;Am Health Drug Benefits,2017

2. Association between serum potassium and outcomes in patients with reduced kidney function.;Luo;Clin J Am Soc Nephrol,2016

3. The frequency of hyperkalemia and its significance in chronic kidney disease.;Einhorn;Arch Intern Med,2009

4. Diabetes and drug-associated hyperkalemia: effect of potassium monitoring.;Raebel;J Gen Intern Med,2010

5. K/DOQI clinical practice guidelines on hypertension and antihypertensive agents in chronic kidney disease.;Kidney Disease Outcomes Quality Initiative (K/DOQI);Am J Kidney Dis,2004

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