基于小波神经网络时间序列模型预测血药浓度的研究

Prediction of Plasma Concentration Based on Wavelet Neural Network Time Series Model

关键词:  小波神经网络  时间序列  血药浓度  药动学  MATLAB

Key words:  wavelet neural network  time series  plasma concentration  pharmacokinetics  MATLAB

摘要:

目的 基于小波神经网络时间序列模型预测阿司匹林血药浓度，评价模型的适应性。方法 对4组家兔灌胃，分别于0.15，0.25，0.5，1.0，1.5，2.0，2.5，3.0，3.5，4.0，6.0，13.0，22.0 h时间点获取血药浓度数据，利用计算机软件MATLAB对其中3组实验数据进行网络训练，利用训练好的网络对剩下的1组血药浓度数据进行预测。之后联合药动学，判断其房室模型和药动学特点。结果 模拟仿真结果与实际数据基本符合，网络的绝对平均误差范围在0.3%~5.39%，在统计学允许误差范围之内。联合药动学仿真证明了阿司匹林的血管外给药药动学特点是二室模型。结论 小波神经网络时间序列模型在预测阿司匹林血药浓度时有较好拟合能力和优良预测能力，同时与药动学的结合更为现代临床药理的研究起到积极的推动作用。

Abstract:

OBJECTIVE To evaluate the adaptability of wavelet neural network time series model through predicting the concentration of aspirin in blood. METHODS Four groups of rabbits were fed with aspirin, and plasma concentration data were obtained in 0.15, 0.25, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 6.0, 13.0, 22.0 h time points. Then through the software MATLAB, 3 groups of experimental data were trained by network, and the trained network was used to predict the plasma concentration data of the left one group. Its characteristics of the compartment model and pharmacokinetic were determined by combination of pharmacokinetics. RESULTS The simulation results were consistent with the actual data, and the absolute mean error of the network was in the range of 0.3%-5.39%. The pharmacokinetics of aspirin in two compartment model was proved by combination of pharmacokinetic simulation. CONCLUSION Wavelet neural network time series model in predicting the plasma concentration of aspirin are with good fitting capability and excellent predictive ability, at the same time with the combination of pharmacokinetics plays a more positive role in promoting modern clinical pharmacology research.