Predication of plasma concentration of remifentanil based on Elman neural network

Due to the nature of ultra-short-acting opioid remifentanil of high time-varying, complex compartment model and low-accuracy of plasma concentration prediction, the traditional estimation method of population pharmacokinetics parameters, nonlinear mixed effects model (NONMEM), has the abuses of tedious work and plenty of man-made jamming factors. The Elman feedback neural network was built. The relationships between the patients’ plasma concentration of remifentanil and time, patient’ age, gender, lean body mass, height, body surface area, sampling time, total dose, and injection rate through network training were obtained to predict the plasma concentration of remifentanil, and after that, it was compared with the results of NONMEM algorithm. In conclusion, the average error of Elman network is −6.34%, while that of NONMEM is 18.99%. The absolute average error of Elman network is 27.07%, while that of NONMEM is 38.09%. The experimental results indicate that Elman neural network could predict the plasma concentration of remifentanil rapidly and stably, with high accuracy and low error. For the characteristics of simple principle and fast computing speed, this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetic and pharmacodynamics.

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