Prediction of Serum Concentration of Oxcarbazepine in Uygur Children with Epilepsy in Xinjiang Based on Artificial Neural Network / 中国药学杂志

ABSTRACT

OBJECTIVE: To predict the steady-state serum concentration of oxcarbazepine in Uygur children with epilepsy in Xinjiang by artificial neural network, thus to provide a theoretical basis for individualized administration of oxcarbazepine. METHODS: The steady-state serum concentration of oxcarbazepine was measured in 270 Uygur children with epilepsy in the People's Hospital of Xinjiang Uygur Autonomous Region, and the relevant data was extracted. The prediction model of plasma concentration of oxcarbazepine was constructed by using Matlab (R2018a) programming software and deep learning network. RESULTS: The network parameters of the model were as follows the initial learning rate was 0.001, the final learning rate was 0.000 1, the momentum coefficient was 0.90, the maximum training times was 1 000, the genetic algebra was 6 000, and the other parameters were default values. The results of model verification showed that among the 45 Uygur children with epilepsy, the prediction errors of 45 oxcarbazepine serum trough concentrations were all less than 10%, and the rate of error of less than 15% was 100.00%. The mean prediction error(MPE) was 0.01% and the mean absolute prediction error(MAE) was 1.21%. The correlation coefficient between the predicted blood concentration and the actual determined concentration was 0.997, and the predicted result was ideal. CONCLUSION: It is feasible to use artificial neural network to predict the serum concentration of oxcarbazepine in Uygur children with epilepsy in Xinjiang. It can be used in the study of individual administration of oxcarbazepine to promote the rational use of oxcarbazepine in clinic.