Remifentanil blood concentration forecast model based on support vector machine with particle swarm optimization / 中国药学杂志

ABSTRACT

OBJECTIVE: To develop a SVM model which is constructed by using particle swarm optimization to a predict the plasma concentration of remifentail. METHODS: This research establishes a PSO-SVM model which is constructed by using particle swarm optimization to a predict the plasma concentration of remifentanil. The model was capable of capturing the nonlinear relationship among plasma concentration, time, and the patient's signs exactly. RESULTS: The average error of PSO-SVM is -1.07%, while that of NONMEM is -2.24%. The absolute average error of PSO-SVM is 9.09%, while that of NONMEM is 19.92%. CONCLUSION: Experimental results indicate that PSO-SVM model could predict the plasma concentration of remifentanil rapidly and stably, with high accuracy and low error. For the characteristic of simple principle and fast computing speed, this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetics and pharmacodynamics.