A pharmacokinetic model based on the SSA-1DCNN-Attention method

Abstract

To solve the problem of the lack of representativeness of the training set and the poor prediction accuracy due to the limited number of training samples when the machine learning method is used for the classification and prediction of pharmacokinetic indicators, this paper proposes a 1DCNN-Attention concentration prediction model optimized by the sparrow search algorithm (SSA). First, the SMOTE method is used to expand the small sample experimental data to make the data diverse and representative. Then a one-dimensional convolutional neural network (1DCNN) model is established, and the attention mechanism is introduced to calculate the weight of each variable for dividing the importance of each pharmacokinetic indicator by the output drug concentration. The SSA algorithm was used to optimize the parameters in the model to improve the prediction accuracy after data expansion. Taking the pharmacokinetic model of phenobarbital (PHB) combined with Cynanchum otophyllum saponins to treat epilepsy as an example, the concentration changes of PHB were predicted and the effectiveness of the method was verified. The results show that the proposed model has a better prediction effect than other methods.