ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation

Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF. We trained...

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Vydané v:	Circulation (New York, N.Y.) Ročník 145; číslo 2; s. 122
Hlavní autori:	Khurshid, Shaan, Friedman, Samuel, Reeder, Christopher, Di Achille, Paolo, Diamant, Nathaniel, Singh, Pulkit, Harrington, Lia X, Wang, Xin, Al-Alusi, Mostafa A, Sarma, Gopal, Foulkes, Andrea S, Ellinor, Patrick T, Anderson, Christopher D, Ho, Jennifer E, Philippakis, Anthony A, Batra, Puneet, Lubitz, Steven A
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States 11.01.2022
Predmet:	Atrial Fibrillation - diagnosis Atrial Fibrillation - pathology Deep Learning - standards Electrocardiography - methods Female Humans Male Middle Aged Risk Factors deep learning atrial fibrillation electronic health records
ISSN:	1524-4539, 1524-4539
On-line prístup:	Zistit podrobnosti o prístupe
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