Using multi-variable grey model optimized by differential evolution algorithm to forecast plasma concentration of propofol
10.16438/j.0513-4870.2017-0381
- VernacularTitle:基于差分进化算法优化多因素灰色模型的异丙酚血药浓度预测
- Author:
Long-yan LI
1
;
Yang CAO
2
;
Ke-jia PAN
3
Author Information
1. Department of Anesthesiology, Xiangya Hospital, Central South University, Changsha 410008, China
2. Center of Medical Engineering, Xiangya Hospital, Central South University, Changsha 410008, China
3. School of Mathematics and Statistics, Central South University, Changsha 410083, China
- Publication Type:ORIGINAL ARTICLES
- Keywords:
differential evolution algorithm;
multi-variable grey model;
propofol;
plasma concentration
- From:
Acta Pharmaceutica Sinica
2017;52(10):1599-1604
- CountryChina
- Language:Chinese
-
Abstract:
Due to the characteristics of propofol of high time-varying, and complex compartment model, the traditional method of nonlinear mixed effects modeling (NONMEM) has miscellaneous of variables and plenty of artificial factors in the estimation of propofol. This study was aimed to build a propofol prediction model based on the differential evolution (DE) algorithm and grey model. DE was used to optimize the pa-rameter of multi-variable grey model (MGM) and to build a model of prediction of the plasma concentration of propofol based on the grey model. It was compared with the results of NONMEM algorithm. In conclusion, the median performance error (MDPE) of DE-MGM was -4.6%, while the result of NONMEM is -12.13%. The median absolute performance error (MDAPE) of GA-BP neural network is 13.19%, while that of NONMEM is 23.12%. The experimental results suggest that the new method is suitable to determine the short half-life of anesthesia drug propofol with higher accuracy.
