本文選題：差分進(jìn)化算法 + 多因素灰色模型��； 參考：《藥學(xué)學(xué)報(bào)》2017年10期

【摘要】：針對(duì)短效靜脈麻醉藥物異丙酚在藥物代謝過(guò)程中存在強(qiáng)時(shí)變性、復(fù)雜非線(xiàn)性等特點(diǎn),以及傳統(tǒng)的群體藥代非線(xiàn)性混合效應(yīng)法在建模方法中存在工作繁雜、人為因素多等缺陷,本研究利用差分進(jìn)化算法優(yōu)化多因素時(shí)序灰色模型,建立基于灰色理論的異丙酚藥代血藥濃度預(yù)測(cè)模型,并與非線(xiàn)性混合效應(yīng)建模法(nonlinear mixed effects modeling,NONMEM)預(yù)測(cè)效果進(jìn)行比較。結(jié)果顯示,差分進(jìn)化-多變量灰色模型(DE-MGM)的預(yù)測(cè)結(jié)果的偏離性(MDPE)為-4.6%,NONMEM為-12.13%;DE-MGM的預(yù)測(cè)結(jié)果的精確度(MDAPE)為13.19%,NONMEM為23.12%�；诓罘诌M(jìn)化優(yōu)化多因素灰色模型能穩(wěn)定預(yù)測(cè)異丙酚血藥濃度,且準(zhǔn)確度高。該方法原理簡(jiǎn)單,實(shí)現(xiàn)便捷,可適用于異丙酚等短效靜脈麻醉藥物的群體藥代藥效學(xué)研究和分析。
[Abstract]:In view of the characteristics of propofol in the course of drug metabolism, such as strong time-varying and complex nonlinearity, the traditional method of group pharmacokinetic nonlinear mixing effect has many defects, such as complicated work and many human factors in the modeling method of short-term intravenous anesthetic drug propofol. In this study, the multi-factor time-series grey model was optimized by using differential evolution algorithm, and the prediction model of propofol drug concentration was established based on grey theory, and compared with nonlinear mixed effects modeling method of NONMEM (nonlinear mixed effects modeling method). The results show that the deviant MDPEs of the DE-MGMGM prediction results are -4.6NMEM -12.13DE-MGM, the accuracy of the prediction results is 13.19NONMEM is 23.120.The results show that the deviation of MDPEs is -4.6NMEM and -12.13DE-MGM, and the accuracy of the prediction results is 13.19NMEM (23.12NMEM). Multi-factor grey model based on differential evolution can predict propofol concentration stably and has high accuracy. The method is simple in principle and convenient in realization. It can be applied to the study and analysis of population pharmacodynamics of propofol and other short-acting intravenous anesthetic drugs.
【作者單位】： 中南大學(xué)湘雅醫(yī)院麻醉科;中南大學(xué)湘雅醫(yī)院醫(yī)學(xué)工程中心;中南大學(xué)數(shù)學(xué)與統(tǒng)計(jì)學(xué)院;
【基金】：國(guó)家自然科學(xué)青年基金資助項(xiàng)目(81601728)
【分類(lèi)號(hào)】：R969
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本文編號(hào)：1907186