肾小球滤过率估算模型研究  

Research of the Glomerular Filtration Rate Estimation Model

作　　者：杨万元[1] 王胜男[1] 赵卫红[2] 裴小华[2] 李冬[1] 朱近[1] 

机构地区：[1]南京理工大学计算机科学与技术学院,南京210094 [2]江苏省人民医院,南京210029

出　　处：《生物医学工程学杂志》2013年第5期963-967,共5页Journal of Biomedical Engineering

基　　金：江苏省科技创新与成果转化(生命健康科技)基金资助项目(BL201266)

摘　　要：肾小球滤过率(GFR)是重要肾功能指标,能精确定量反映肾功能,具有准确、灵敏、稳定,且重复性好的特点。目前GFR主要用放射性同位素标记物99mTc-DTPA清除率来测定,测定过程繁琐,时间较长,费用高,遭受放射性损伤。用其它易于获取的肾功能参数,通过预测模型估算GFR是当前国际上正在研究的方法。针对中国人群数据,本文对已有的三种GFR的计算模型,用多元函数优化技术进行了模型系数优化,提高了计算准确性;并用BP神经网络技术建立了新的GFR估算模型,经过计算,新模型的均方根误差和P30误差均优于原公式,得到了更为简单和高精度的结果。The glomerular filtration rate （GFR） is an important index of renal function with advantages of accurate, sensitive, stable, repetitive and accurate measurement of renal functions. The GFR is mainly determined in the clear rate of radioisotope markers 99mTe-DTPA, the process of which is complicated, long time-taking, high cost, and ra- dioactively injuring. Recently, the methods using other renal function parameters measured easily to create the math- ematic models and to estimate GFR are being investigated in the world. In this paper, for the renal function data of the Chinese, the efficiency in the three existing GFR formulas has been optimized with multi-function optimization techniques and the accuracy of the computation has been improved. Then the BP neural network technology is used for establishing a new GFR formula, which is a simpler form and obtains higher precision result than the formulas existed. The rmse and P30 of the new formula are better than those of the old ones.

关 键 词：肾小球滤过率 数学模型 优化算法 BP神经网络 

分 类 号：R692[医药卫生—泌尿科学]