Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction

Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction

Background This study evaluated the role of supplementing Society of Thoracic Surgeons (STS) risk models for surgical aortic valve replacement with machine learning (ML). Methods and Results Adults undergoing isolated surgical aortic valve replacement in the STS National Database between 2007 and 20...

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Autores principales: Arman Kilic, Robert H. Habib, James K. Miller, David M. Shahian, Joseph A. Dearani, Artur W. Dubrawski
Formato: article
Lenguaje:EN
Publicado: Wiley 2021
Materias:
aortic valve replacement
complications
machine learning
mortality
risk prediction
Diseases of the circulatory (Cardiovascular) system
RC666-701
Acceso en línea:https://doaj.org/article/3861eabb8530449181e695aa601676a0
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https://doaj.org/article/3861eabb8530449181e695aa601676a0

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