Prediction of All-Cause Mortality Following Percutaneous Coronary Intervention in Bifurcation Lesions Using Machine Learning Algorithms

Prediction of All-Cause Mortality Following Percutaneous Coronary Intervention in Bifurcation Lesions Using Machine Learning Algorithms

Stratifying prognosis following coronary bifurcation percutaneous coronary intervention (PCI) is an unmet clinical need that may be fulfilled through the adoption of machine learning (ML) algorithms to refine outcome predictions. We sought to develop an ML-based risk stratification model built on cl...

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Bibliographic Details
Main Authors: Jacopo Burrello, Guglielmo Gallone, Alessio Burrello, Daniele Jahier Pagliari, Eline H. Ploumen, Mario Iannaccone, Leonardo De Luca, Paolo Zocca, Giuseppe Patti, Enrico Cerrato, Wojciech Wojakowski, Giuseppe Venuti, Ovidio De Filippo, Alessio Mattesini, Nicola Ryan, Gérard Helft, Saverio Muscoli, Jing Kan, Imad Sheiban, Radoslaw Parma, Daniela Trabattoni, Massimo Giammaria, Alessandra Truffa, Francesco Piroli, Yoichi Imori, Bernardo Cortese, Pierluigi Omedè, Federico Conrotto, Shao-Liang Chen, Javier Escaned, Rosaly A. Buiten, Clemens Von Birgelen, Paolo Mulatero, Gaetano Maria De Ferrari, Silvia Monticone, Fabrizio D’Ascenzo
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Journal of Personalized Medicine
Subjects:
percutaneous coronary intervention
coronary bifurcation
machine learning
prognosis
Online Access:https://www.mdpi.com/2075-4426/12/6/990

Internet

https://www.mdpi.com/2075-4426/12/6/990

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