Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care

Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care

AbstractMajor adverse kidney events within 30 d (MAKE30) implicates poor outcomes for elderly patients in the intensive care unit (ICU). This study aimed to predict the occurrence of MAKE30 in elderly ICU patients using machine learning. The study cohort comprised 2366 elderly ICU patients admitted...

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Bibliographic Details
Main Authors: Lin Wang, Shao-Bin Duan, Ping Yan, Xiao-Qin Luo, Ning-Ya Zhang
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:Renal Failure
Subjects:
Critical illness
major adverse kidney events
the elderly
XGBoost
machine learning
Online Access:https://www.tandfonline.com/doi/10.1080/0886022X.2023.2215329

Internet

https://www.tandfonline.com/doi/10.1080/0886022X.2023.2215329

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