Machine learning models for prediction of adverse events after percutaneous coronary intervention

Machine learning models for prediction of adverse events after percutaneous coronary intervention

Abstract An accurate prediction of major adverse events after percutaneous coronary intervention (PCI) improves clinical decisions and specific interventions. To determine whether machine learning (ML) techniques predict peri-PCI adverse events [acute kidney injury (AKI), bleeding, and in-hospital m...

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Bibliographic Details
Main Authors: Nozomi Niimi, Yasuyuki Shiraishi, Mitsuaki Sawano, Nobuhiro Ikemura, Taku Inohara, Ikuko Ueda, Keiichi Fukuda, Shun Kohsaka
Format: Article
Language:English
Published: Nature Portfolio 2022-04-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-022-10346-1

Internet

https://doi.org/10.1038/s41598-022-10346-1

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