Simple Models Versus Deep Learning in Detecting Low Ejection Fraction From The Electrocardiogram

Hughes, J. W., Somani, S., Elias, P., Tooley, J., Rogers, A. J., Poterucha, T., Haggerty, C. M., Salerno, M., Ouyang, D., Ashley, E., Zou, J., & Perez, M. V. (2024). Simple models vs. deep learning in detecting low ejection fraction from the electrocardiogram. European Heart Journal - Digital Health, 5(4), 427–434. https://doi.org/10.1093/ehjdh/ztae034
Authors:
J. Weston Hughes
Sulaiman Somani
Pierre Elias
James Tooley
Albert J. Rogers
Timothy J. Poterucha
Christopher M. Haggerty
Michael Salerno
David Ouyang
Euan A. Ashley
James Zou
Marco Pérez
Affiliated Authors:
Pierre Elias
Timothy J. Poterucha
Christopher M. Haggerty
Columbia Affiliation:
Subjects:
Analysis of Electrocardiogram Signals (OpenAlex Topic)
Analysis and Applications of Heart Rate Variability (OpenAlex Topic)
Author Keywords:
artificial intelligence
deep learning
electrocardiograms
explainability
interpretability
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Publication Type:
Article
Unique ID:
10.1093/ehjdh/ztae034
DOI:
10.1093/ehjdh/ztae034
Journal:
European Heart Journal: Digital Health
Publication Date:
Data Source:
OpenAlex
Source Link:
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Record Created: