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

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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
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  Data: Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation.
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  Data: <searchLink fieldCode="AU" term="%22Yuan+N%22">Yuan N</searchLink>; Department of Medicine, University of California, San Francisco.; Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California.<br /><searchLink fieldCode="AU" term="%22Duffy+G%22">Duffy G</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Dhruva+SS%22">Dhruva SS</searchLink>; Department of Medicine, University of California, San Francisco.; Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California.<br /><searchLink fieldCode="AU" term="%22Oesterle+A%22">Oesterle A</searchLink>; Department of Medicine, University of California, San Francisco.; Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California.<br /><searchLink fieldCode="AU" term="%22Pellegrini+CN%22">Pellegrini CN</searchLink>; Department of Medicine, University of California, San Francisco.; Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California.<br /><searchLink fieldCode="AU" term="%22Theurer+J%22">Theurer J</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Vali+M%22">Vali M</searchLink>; Department of Medicine, University of California, San Francisco.; Division of General Internal Medicine, San Francisco Veterans Affairs Medical Center, San Francisco, California.<br /><searchLink fieldCode="AU" term="%22Heidenreich+PA%22">Heidenreich PA</searchLink>; Division of Cardiology, Palo Alto Veterans Affairs Medical Center, Palo Alto, California.; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Palo Alto, California.<br /><searchLink fieldCode="AU" term="%22Keyhani+S%22">Keyhani S</searchLink>; Department of Medicine, University of California, San Francisco.; Division of General Internal Medicine, San Francisco Veterans Affairs Medical Center, San Francisco, California.<br /><searchLink fieldCode="AU" term="%22Ouyang+D%22">Ouyang D</searchLink>; Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California.; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California.
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  Data: <searchLink fieldCode="JN" term="%22101676033%22">JAMA cardiology</searchLink> [JAMA Cardiol] 2023 Dec 01; Vol. 8 (12), pp. 1131-1139.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22American+Medical+Association%22">American Medical Association </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101676033 <i>Publication Model: </i>Print <i>Cited Medium: </i>Internet <i>ISSN: </i>2380-6591 (Electronic) <i>NLM ISO Abbreviation: </i>JAMA Cardiol <i>Subsets: </i>MEDLINE
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        Value: 10.1001/jamacardio.2023.3701
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              Text: 2023 Dec 01
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