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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Bibliographic Details
Main Authors: Arman Kilic, Robert H. Habib, James K. Miller, David M. Shahian, Joseph A. Dearani, Artur W. Dubrawski
Format: Article
Language:English
Published: Wiley 2021-11-01
Series:Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
Subjects:
aortic valve replacement
complications
machine learning
mortality
risk prediction
Online Access:https://www.ahajournals.org/doi/10.1161/JAHA.120.019697

Internet

https://www.ahajournals.org/doi/10.1161/JAHA.120.019697

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