Machine learning models in predicting graft survival in kidney transplantation: meta-analysis.

ABSTRACT:

Background

The variations in outcome and frequent occurrence of kidney allograft failure continue to pose important clinical and research challenges despite recent advances in kidney transplantation. The aim of this systematic review was to examine the current application of machine learning models in kidney transplantation and perform a meta-analysis of these models in the prediction of graft survival.

Methods

This review was registered with the PROSPERO database (CRD42021247469) and all peer-reviewed original articles that reported machine learning model-based prediction of graft survival were included. Quality assessment was performed by the criteria defined by Qiao and risk-of-bias assessment was performed using the PROBAST tool. The diagnostic performance of the meta-an

SUBMITTER: Ravindhran B 

PROVIDER: S-EPMC10050937 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature