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

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 mortality]...

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Detalles Bibliográficos
Autores principales: Niimi, Nozomi, Shiraishi, Yasuyuki, Sawano, Mitsuaki, Ikemura, Nobuhiro, Inohara, Taku, Ueda, Ikuko, Fukuda, Keiichi, Kohsaka, Shun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9012739/
https://www.ncbi.nlm.nih.gov/pubmed/35428765
http://dx.doi.org/10.1038/s41598-022-10346-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9012739/
https://www.ncbi.nlm.nih.gov/pubmed/35428765
http://dx.doi.org/10.1038/s41598-022-10346-1

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