Evaluation of Glomerular Filtration Rate in Chronic Kidney Disease by Radial Basis Function Neural Network.

Abstract:

OBJECTIVE:To develop a radial basis function (RBF) neural network and investigate its performance in the estimation of glomerular filtration rate (GFR) for patients with chronic kidney disease. METHODS:A total of 651 patients with chronic kidney disease were enrolled in this study. The GFR measured by 99mTc-DTPA renal dynamic imaging was used as the standard GFR. The RBF neural network model was established and the performance prediction GFR value was verified. It was found that the RBF neural network could better evaluate the GFR of patients with chronic kidney disease stage 2-5, which is superior to the Modification of Diet in Renal Disease equation. CONCLUSIONS:The RBF neural network evaluated GFR significantly for patients with chronic kidney disease stages 2-5, and it showed no difference with the 99mTc-DTPA renal dynamic imaging method, and it can be used for estimated GFR evaluation.

journal_name

Transplant Proc

authors

Xu J,Guo B,Liu C

doi

10.1016/j.transproceed.2020.01.008

subject

Has Abstract

pub_date

2020-04-01 00:00:00

pages

748-753

issue

3

eissn

0041-1345

issn

1873-2623

pii

S0041-1345(19)30844-9

journal_volume

52

pub_type

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