Machine Learning For Identification Of Electrocardiographic Measures Predicting Atrial Fibrillation: The Multi-Ethnic Study Of Atherosclerosis (Mesa)

Eric Xie,Colin Wu,Mohammad Ostovaneh,Wendy S. Post,Shelby Kutty,Elsayed Soliman,David A. Bluemke,Susan R. Heckbert,Joao A.C. Lima,Bharath Ambale Venkatesh

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY（2020）

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摘要

Electrocardiography (ECG) is a practical and cost-effective clinical tool. Machine learning may enable identification of novel ECG variables for predicting incident atrial fibrillation (AF). We included 3151 participants from MESA with 568 baseline ECG variables each in addition to demographic,

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atrial fibrillation,electrocardiographic measures,atherosclerosis,machine learning,multi-ethnic

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