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

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 mode...

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Bibliographic Details
Main Authors: Chenxi Huang, Karthik Murugiah, Shiwani Mahajan, Shu-Xia Li, Sanket S Dhruva, Julian S Haimovich, Yongfei Wang, Wade L Schulz, Jeffrey M Testani, Francis P Wilson, Carlos I Mena, Frederick A Masoudi, John S Rumsfeld, John A Spertus, Bobak J Mortazavi, Harlan M Krumholz
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2018-11-01
Series:PLoS Medicine
Online Access:http://europepmc.org/articles/PMC6258473?pdf=render

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