Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study

BACKGROUND: The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether mod...

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Detalles Bibliográficos
Autores principales: Huang, Chenxi, Murugiah, Karthik, Mahajan, Shiwani, Li, Shu-Xia, Dhruva, Sanket S., Haimovich, Julian S., Wang, Yongfei, Schulz, Wade L., Testani, Jeffrey M., Wilson, Francis P., Mena, Carlos I., Masoudi, Frederick A., Rumsfeld, John S., Spertus, John A., Mortazavi, Bobak J., Krumholz, Harlan M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6258473/
https://www.ncbi.nlm.nih.gov/pubmed/30481186
http://dx.doi.org/10.1371/journal.pmed.1002703

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6258473/
https://www.ncbi.nlm.nih.gov/pubmed/30481186
http://dx.doi.org/10.1371/journal.pmed.1002703

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