Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation.

Saved in:
Bibliographic Details
Title: Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation.
Authors: Yuan N; Department of Medicine, University of California, San Francisco.; Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California., Duffy G; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California.; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California., Dhruva SS; Department of Medicine, University of California, San Francisco.; Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California., Oesterle A; Department of Medicine, University of California, San Francisco.; Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California., Pellegrini CN; Department of Medicine, University of California, San Francisco.; Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California., Theurer J; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California.; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California., Vali M; Department of Medicine, University of California, San Francisco.; Division of General Internal Medicine, San Francisco Veterans Affairs Medical Center, San Francisco, California., Heidenreich PA; Division of Cardiology, Palo Alto Veterans Affairs Medical Center, Palo Alto, California.; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Palo Alto, California., Keyhani S; Department of Medicine, University of California, San Francisco.; Division of General Internal Medicine, San Francisco Veterans Affairs Medical Center, San Francisco, California., Ouyang D; Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California.; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California.
Source: JAMA cardiology [JAMA Cardiol] 2023 Dec 01; Vol. 8 (12), pp. 1131-1139.
Publication Type: Journal Article; Research Support, Non-U.S. Gov't; Research Support, N.I.H., Extramural
Journal Info: Publisher: American Medical Association Country of Publication: United States NLM ID: 101676033 Publication Model: Print Cited Medium: Internet ISSN: 2380-6591 (Electronic) NLM ISO Abbreviation: JAMA Cardiol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2380-6591
DOI:10.1001/jamacardio.2023.3701