Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models

S Senanayake, N White, N Graves, H Healy… - International journal of …, 2019 - Elsevier
Introduction Machine learning has been increasingly used to develop predictive models to
diagnose different disease conditions. The heterogeneity of the kidney transplant population …

Risk prediction models for graft failure in kidney transplantation: a systematic review

R Kaboré, MC Haller, J Harambat… - Nephrology Dialysis …, 2017 - academic.oup.com
Risk prediction models are useful for identifying kidney recipients at high risk of graft failure,
thus optimizing clinical care. Our objective was to systematically review the models that have …

Predicting graft survival among kidney transplant recipients: A Bayesian decision support model

K Topuz, FD Zengul, A Dag, A Almehmi… - Decision Support …, 2018 - Elsevier
Predicting the graft survival for kidney transplantation is a high stakes undertaking
considering the shortage of available organs and the utilization of healthcare resources. The …

Prediction of delayed graft function after kidney transplantation: comparison between logistic regression and machine learning methods

A Decruyenaere, P Decruyenaere, P Peeters… - BMC medical informatics …, 2015 - Springer
Background Predictive models for delayed graft function (DGF) after kidney transplantation
are usually developed using logistic regression. We want to evaluate the value of machine …

The future role of machine learning in clinical transplantation

KL Connor, ED O'Sullivan, LP Marson… - …, 2021 - journals.lww.com
The use of artificial intelligence and machine learning (ML) has revolutionized our daily lives
and will soon be instrumental in healthcare delivery. The rise of ML is due to multiple factors …

A theoretical and computational equilibria analysis of a multi-player kidney exchange program

M Carvalho, A Lodi - European Journal of Operational Research, 2023 - Elsevier
A main aim of kidney exchange programs (KEPs) is to maximize the number of transplants
within a pool of incompatible patient-donor pairs by exchanging donors. A KEP involving …

A simple tool to predict outcomes after kidney transplant

BL Kasiske, AK Israni, JJ Snyder, MA Skeans… - American journal of …, 2010 - Elsevier
BACKGROUND:: Surprisingly few tools have been developed to predict outcomes after
kidney transplant. STUDY DESIGN:: Retrospective observational cohort study. SETTING & …

An end stage kidney disease predictor based on an artificial neural networks ensemble

T Di Noia, VC Ostuni, F Pesce, G Binetti, D Naso… - Expert systems with …, 2013 - Elsevier
Abstract IgA Nephropathy (IgAN) is a worldwide disease that affects kidneys in human
beings and leads to end-stage kidney disease (ESKD) thus requiring renal replacement …

Predicting kidney transplantation outcome based on hybrid feature selection and KNN classifier

DM Atallah, M Badawy, A El-Sayed… - Multimedia Tools and …, 2019 - Springer
Kidney transplantation outcome prediction is very significant and doesn't require emphasis.
This will grant the selection of the best available kidney donor and the best …

[HTML][HTML] Artificial intelligence and kidney transplantation

N Seyahi, SG Ozcan - World Journal of Transplantation, 2021 - ncbi.nlm.nih.gov
Artificial intelligence and its primary subfield, machine learning, have started to gain
widespread use in medicine, including the field of kidney transplantation. We made a review …