基于径向基函数神经网络评估慢性肾脏病患者的肾小球滤过率
Evaluation of Glomerular Filtration Rate in Chronic Kidney Disease by Radial Basis Function Neural Network.
作者信息
Xu Jing, Guo Buyuan, Liu Chunyan
机构信息
Yantai Yuhuangding Hospital, Yantai City, Shandong Province, China.
The Second Affiliated Hospital of Dalian Medical University, Dalian City, Liaoning Province, China.
出版信息
Transplant Proc. 2020 Apr;52(3):748-753. doi: 10.1016/j.transproceed.2020.01.008. Epub 2020 Mar 19.
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 Tc-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 Tc-DTPA renal dynamic imaging method, and it can be used for estimated GFR evaluation.
目的
构建径向基函数（RBF）神经网络并研究其在慢性肾脏病患者肾小球滤过率（GFR）估计中的性能。
方法
本研究共纳入651例慢性肾脏病患者。将经Tc-DTPA肾动态显像测得的GFR作为标准GFR。建立RBF神经网络模型并验证其预测GFR值的性能。发现RBF神经网络能更好地评估2-5期慢性肾脏病患者的GFR，优于肾脏病饮食改良公式。
结论
RBF神经网络对2-5期慢性肾脏病患者的GFR评估具有显著意义，与Tc-DTPA肾动态显像法无差异，可用于GFR估计评估。
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