Journal Articles by C. Haggerty

X. Zhang, A. E. U. Cerna, J. V. Stough, Y. Chen, B. J. Carry, A. Alsaid, S. Raghunath, D. P. vanMaanen, et al.
Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset
The International Journal of Cardiovascular Imaging, 2022
E.-M. Yasser, M. Abbas, I. Hoaglund, A. U. Cerna, T. B. Morland, C. M. Haggerty, E. S. Hall, and B. K. Fornwalt
OASIS+: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality
BMC medical informatics and decision making, 21(1), 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
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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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
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