摘要
建立人工神经网络用于估算他克莫司血药浓度。收集37例肝移植受者口服他克莫司的176份稳态全血浓度数据,采用遗传算法配合动量法优化网络参数,建立人工神经网络。人工神经网络平均预测误差(MPE)与平均绝对误差(MAE)分别为(0.02±2.40)ng.mL 1和(1.93±1.37)ng.mL 1,84.6%血药浓度数据绝对预测误差≤3.0 ng.mL 1。人工神经网络的准确性及精密度优于多元线性回归。结果表明,人工神经网络预测的相关性、准确性和精密度较好,简便迅捷,可用于预测他克莫司血药浓度。
This study is to establish an artificial neural network （ANN） for predicting blood tacrolimus concentration in liver transplantation recipients. Tacrolimus concentration samples （176 samples） from 37 Chinese liver transplantation recipients were collected. ANN established after network parameters were optimized by using momentum method combined with genetic algorithm. Furthermore, the performance of ANN was compared with that of multiple linear regression （MLR）. When using accumulated dose of 4 days before therapeutic drug monitoring （TDM） of tacrolimus concentration as input factor, mean prediction error and mean absolute prediction error of ANN were 0.02 ± 2.40 ng.mL-1 and 1.93 ± 1.37 ng＇mL-1, respectively. The absolute prediction error of 84.6% of testing data sets was less than 3.0 ng mL-1. Accuracy and precision of ANN are superior to those of MLR. The correlation, accuracy and precision of ANN are good enough to predict blood tacrolimus concentration.
出处
《药学学报》
CAS
CSCD
北大核心
2012年第9期1134-1140,共7页
Acta Pharmaceutica Sinica
关键词
他克莫司
肝移植
人工神经网络
tacrolimus
liver transplantation
artificial neural network
