Medicine Safety Assessment Method Based on Dynamic Dual Optimization

Abstract

As people pay more and more attention to medicine safety issues, related medicine safety monitoring platforms are also rapidly popularized. However, previous work has poor accuracy and low efficiency in medicine safety assessment. In this paper, the medicine safety evaluation index system of the medicine safety monitoring platform is determined from four aspects: medicine research and development, medicine market, medicine production, and medicine uses. In order to solve the problems of the medicine safety evaluation model, such as low evaluation accuracy, slow convergence speed, and long training time, the dynamic dual optimization of PSO-BP medicine safety assessment method (OPSO-BP) is proposed. The weights and thresholds of BP neural network are optimized by the PSO algorithm to improve the quality of assessment. In addition, we optimize PSO: use the cosine function to dynamically adjust the inertia weight w and use the average optimal position of the individual in the population to replace the optimal position of the individual. It improves the problem that the evaluation model in the traditional algorithm is easy to fall into the local optimal solution due to the lack of generalization ability. In this paper, the effectiveness of OPSO-BP is verified by comparative experiments with the designed questionnaire data of medicine safety evaluation.