Machine Learning Improves Upon Clinicians' Prediction of End Stage Kidney Disease

Machine Learning Improves Upon Clinicians' Prediction of End Stage Kidney Disease

Background and ObjectivesChronic kidney disease progression to ESKD is associated with a marked increase in mortality and morbidity. Its progression is highly variable and difficult to predict.MethodsThis is an observational, retrospective, single-centre study. The cohort was patients attending hosp...

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Bibliographic Details
Main Authors: Aaron Chuah, Giles Walters, Daniel Christiadi, Krishna Karpe, Alice Kennard, Richard Singer, Girish Talaulikar, Wenbo Ge, Hanna Suominen, T. Daniel Andrews, Simon Jiang
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-03-01
Series:Frontiers in Medicine
Subjects:
machine learning (ML)
prediction model
end stage kidney disease (ESKD)
XGBoost (Extreme Gradient Boosting)
chronic kidney disease
Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2022.837232/full

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https://www.frontiersin.org/articles/10.3389/fmed.2022.837232/full

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