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Using multi-variable grey model optimized by differential evolution algorithm to forecast plasma concentration of propofol
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Long-yan LI1, Yang CAO2, *, Ke-jia PAN3
Acta Pharmaceutica Sinica | 2017, 52(10) : 1599 - 1604
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Acta Pharmaceutica Sinica | 2017, 52(10): 1599-1604
• ORIGINAL ARTICLES •
Using multi-variable grey model optimized by differential evolution algorithm to forecast plasma concentration of propofol
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Long-yan LI1, Yang CAO2, *, Ke-jia PAN3
Affiliations
  • 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
Published: 2017-10-12 doi: 10.16438/j.0513-4870.2017-0381
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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.

differential evolution algorithm  /  multi-variable grey model  /  propofol  /  plasma concentration
Long-yan LI, Yang CAO, Ke-jia PAN. Using multi-variable grey model optimized by differential evolution algorithm to forecast plasma concentration of propofol[J]. Acta Pharmaceutica Sinica, 2017 , 52 (10) : 1599 -1604 . DOI: 10.16438/j.0513-4870.2017-0381
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Article Info
doi: 10.16438/j.0513-4870.2017-0381
  • Receive Date：2017-04-30
  • Online Date：2026-01-14
  • Published：2017-10-12
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History
  • Received：2017-04-30
  • Revised：2017-07-14
Funding
Affiliations
    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
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表12种不同金属材料的力学参数
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Family		 属数
Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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