Rough set theory is a powerful tool to deal with incomplete information system, which can be applied to prescription data analysis. In this paper, we suggested an improved rough set model called WVP-T model. The model combined the variable precision model with the tolerance relation model. It can overcome the shortcoming of classical model. Furthermore, attribute importance and entropy of information were combined as heuristic information. Medicine was mapped to rough set attribute in order to value its importance. Then, combined with curative effect, attribute reduction was used to investigate the relationship between prescription and medicine and the relationship between symptom and syndrome. The experimental results showed that algorithm proposed in this paper can be used in prescription data analysis and can accurately reveal the compatibility rules to guide the clinical medication.

Objective To prevent and treat of ceramic membrane purification of membrane fouling process of TCM extracts; To explore new methods of forecasting membrane fouling degree.Methods BP neural network model was improved. Methods to fast determine the optimal number of neurons in the hidden layer and fast algorithm for optimizing the weight and threshold of BP neural network were studied. Data of 207 groups of TCM extracts were under network training and prediction.ResultsCompared with the models of multiple regression analysis, basic BP neural network and RBF neural network, the error of the improved BP neural network model was less than that of the BP neural network model, and the mean square error was only 0.0057. In addition, the improved BP neural network model performance was more stable. In the 20 random running experiments, the goal of the success rate achieved up to 95%.Conclusion The improved model has a good network performance, the fitting effect and prediction ability, and can forecast the fouling degree of membrane stably and accurately.

OBJECTIVE Taking Professor Zhou Zhongying's clinical cases of treating hyperthyroidism as the research object,this article explored the use of the TabNet model based on neural networks to discover the diagnosis and treatment rules of hyperthyroid-ism,providing a method reference for inheriting the academic thoughts of famous veteran traditional Chinese medicine practitioners and assisting clinical diagnosis and treatment.METHODS Based on the clinical diagnosis and treatment cases of hyperthyroidism of Pro-fessor Zhou Zhongying and his team,standardized and structured training data were constructed;algorithms based on attention mecha-nism and sparse feature selection mechanism were studied;a pathogenesis prediction model was constructed by inputting standardized clinical manifestations,standardized tongue and pulse conditions;core symptoms,pathogenesis and medication were analyzed,as well as the relationship between the three.RESULTS The trained prediction model was used to predict the 6 pathogenesis of liver stagna-tion,liver fire,phlegm fluid,kidney deficiency,yin deficiency,and blood stasis.Compared with multi-label classification models constructed by classic algorithms such as decision trees and random forests,this model had better classification and prediction indica-tors.Mining was carried out through the decision tree algorithm,and 6 core pathogenesis corresponding Chinese medicine groups were summarized:vinegar-baked Bupleurum chinense,prunella vulgaris,oyster,processed Carapax trionycis,Scrophularia ningpoensis,Asparagus cochinchinensis,Ophiopogon japonicus,etc.CONCLUSION Using the TabNet algorithm on clinical medical record data to build a pathogenesis prediction model based on clinical manifestations,tongue and pulse conditions can effectively predict the core pathogenesis,and then discover the connection between symptoms,pathogenesis and medication,providing methodological references for the inheritance of academic ideas of famous veteran traditional Chinese medicine practitioners and clinical auxiliary diagnosis and treatment decision-making.