Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4
Abstract Introduction End-stage kidney disease (ESKD) is associated with increased morbidity and mortality. Identifying patients with stage 4 CKD (CKD4) at risk of rapid progression to ESKD remains challenging. Accurate prediction of CKD4 progression can improve patient outcomes by improving advance...
Main Authors: | Kullaya Takkavatakarn, Wonsuk Oh, Ella Cheng, Girish N Nadkarni, Lili Chan |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2023-12-01
|
Series: | BMC Nephrology |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12882-023-03424-7 |
Similar Items
-
Validation of the kidney failure risk equation for end-stage kidney disease in Southeast Asia
by: Yeli Wang, et al.
Published: (2019-12-01) -
Predictive Value of Serum Hepcidin Levels for the Risk of Incident End-Stage Kidney Disease in Patients with Chronic Kidney Disease: The KNOW-CKD
by: Sang Heon Suh, et al.
Published: (2024-10-01) -
The Future for End-Stage Kidney Disease Treatment: Implantable Bioartificial Kidney Challenge
by: Federico Nalesso, et al.
Published: (2024-01-01) -
Mesenchymal stem cells therapy in children with end-stage kidney disease
by: Eka Laksmi Hidayati, et al.
Published: (2022-06-01) -
Machine Learning Improves Upon Clinicians' Prediction of End Stage Kidney Disease
by: Aaron Chuah, et al.
Published: (2022-03-01)