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...

Descripción completa

Guardado en:

Detalles Bibliográficos
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
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Sea el primero en dejar un comentario!

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

Ejemplares similares