2021


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
PDF, RIS, BibTex
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
PDF, RIS, BibTex
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
PDF, Supplementary Material, RIS, BibTex
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
URL, DOI, RIS, BibTex