Last Updated: 09/26/2026
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Found 12 publications
Multicenter validation of an artificial intelligence-enabled ECG model to predict 1-year risk of atrial fibrillation or flutter.
Journal: Heart rhythm
Published: January 22, 2026
A probabilistic approach for building disease phenotypes across electronic health records.
Journal: BioData mining
Published: February 13, 2025
An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk.
Journal: Journal of electrocardiology
Published: May 14, 2022
rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography.
Journal: Circulation
Published: May 09, 2022
Promise and Frustration: Machine Learning in Cardiology.
Journal: Circulation. Cardiovascular imaging
Published: June 15, 2021
Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke.
Journal: Circulation
Published: February 16, 2021
Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality.
Journal: Nature biomedical engineering
Published: April 16, 2020
Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network.
Journal: Nature medicine
Published: March 29, 2019
Quantifying Sex Differences in Cardiovascular Care Among Patients Evaluated for Suspected Ischemic Heart Disease.
Journal: Journal of women's health (2002)
Published: December 14, 2018
Association of Patient Beliefs and Preferences With Subsequent Testing After Initial Evaluation for Ischemic Heart Disease.
Journal: JACC. Cardiovascular imaging
Published: July 26, 2016
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Last Updated: 09/26/2026