Predictive modeling for acute kidney injury after percutaneous coronary intervention in patients with acute coronary syndrome: a machine learning approach

Predictive modeling for acute kidney injury after percutaneous coronary intervention in patients with acute coronary syndrome: a machine learning approach

Abstract Background Acute kidney injury (AKI) is one of the preventable complications of percutaneous coronary intervention (PCI). This study aimed to develop machine learning (ML) models to predict AKI after PCI in patients with acute coronary syndrome (ACS). Methods This study was conducted at Teh...

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Bibliographic Details
Main Authors: Amir Hossein Behnoush, M. Moein Shariatnia, Amirmohammad Khalaji, Mahsa Asadi, Alireza Yaghoobi, Malihe Rezaee, Hamidreza Soleimani, Ali Sheikhy, Afsaneh Aein, Somayeh Yadangi, Yaser Jenab, Farzad Masoudkabir, Mehdi Mehrani, Mina Iskander, Kaveh Hosseini
Format: Article
Language:English
Published: BMC 2024-01-01
Series:European Journal of Medical Research
Subjects:
Acute coronary syndrome
Percutaneous coronary intervention
Acute kidney injury
Machine learning
Prediction
Online Access:https://doi.org/10.1186/s40001-024-01675-0

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