| 应用RBF激励WASD神经网络估算GFR |
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| 引用本文：张雨浓，何良宇，刘迅，肖争利，晏小刚.应用RBF激励WASD神经网络估算GFR[J].计算技术与自动化,2016,(1):22-26 |
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| 中文摘要:价格低廉与高准确率的矛盾是测量肾小球滤过率（glomerular filtration rate，GFR）中遇到的主要难题。采用径向基函数（radial basis function，RBF）神经网络和权值与结构确定法（weights-and-structure-determination，WASD）相结合的方法，并基于中山大学附属第三医院的患者数据进行神经网络建模，对肾病患者进行肾小球滤过率估算。计算机数值实验结果显示该方法在50%符合率标准下能达到90%的准确率，而传统方程中最优的准确率为68%。 |
| 中文关键词:神经网络 径向基函数 权值与结构确定法 肾小球滤过率 估算 数值实验 |
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| Application of RBF-Activated WASD Neuronet in Estimating GFR |
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| Abstract:The contradiction between low price and high accuracy is the main problem encountered in estimating glomerular filtration rate (GFR). With the combination of RBF neuronet and weights-and-structure-determination (WASD) algorithm, and based on the data of patients from the Third Affiliated Hospital of Sun Yat-sen University, a neuronet model was built to estimate chronic kidney disease (CKD) patients' GFR. Numerical-experiment results show that the presented method can reach 90% accuracy with 50% accuracy as a standard, while the highest accuracy of the traditional equations is 68%. |
| keywords:neuronet radial basis function weights-and-structure-determination glomerular filtration rate estimation numerical-experiment |
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