Journal Articles by L. Jing

S. Raghunath, J. M. Pfeifer, A. Ulloa, A. Nemani, T. Carbonati, L. Jing, D. P. vanMaanen, D. N. Hartzel, et al.
Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead Electrocardiogram and Help Identify Those at Risk of AF-Related Stroke
Circulation, 2021
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A. Ulloa, L. Jing, C. W. Good, S. Raghunath, J. D. Suever, C. D. Nevius, G. J. Wehner, D. N. Hartzel, et al.
Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality
Nature Biomedical Engineering, 2021
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A. E. Ulloa-Cerna, L. Jing, J. M. Pfeifer, S. Raghunath, J. A. Ruhl, D. B. Rocha, J. B. Leader, N. Zimmerman, et al.
rECHOmmend: an ECG-based machine-learning approach for identifying patients at high-risk of undiagnosed structural heart disease detectable by echocardiography
medRxiv, 2021
S. Raghunath, A. Ulloa, L. Jing, J. Stough, D. N. Hartzel, J. B. Leader, H. L. Kirchner, M. C. Stumpe, et al.
Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
Nature medicine, 26(6), 2020
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L. Jing, A. Ulloa, C. W. Good, N. M. Sauers, G. Schneider, D. N. Hartzel, J. B. Leader, H. L. Kirchner, et al.
A machine learning approach to management of heart failure populations
Heart Failure, 8(7), 2020
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M. D. Samad, A. Ulloa, G. J. Wehner, L. Jing, D. Hartzel, C. W. Good, B. A. Williams, C. M. Haggerty, et al.
Predicting survival from large echocardiography and electronic health record datasets: optimization with machine learning
JACC: Cardiovascular Imaging, 2018
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