Journal Articles by D. Hartzel


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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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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Abstracts


A. Ulloa, G. Wehner, D. Hartzel, C. Haggerty, and B. Fornwalt
Data-Driven Phenotyping of Patients with Heart Failure using a Deep-learning Cluster Representation of Echocardiographic and Electronic Health Record Data
AHA scientific sessions, 2017
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M. Samad, A. Ulloa, G. Wehner, D. Hartzel, C. Haggerty, and B. Fornwalt
Machine Learning-Based Classification of Echocardiographic Measurements Significantly Improves Accuracy in Predicting Mortality over Standard Clinical Variables
AHA scientific sessions, 2017
RIS, BibTex