Traditional Chinese Medicine Prescription Recommendation Model Construction Based on Rule Generating Medical Cases and Transformer Algorithm

LIAN Zhirun; ZHANG Jiawei; YANG Baolin

Abstract：Objective To build a prescription recommendation model of traditional Chinese medicine(TCM)about prescriptions in Shang Han Lun.To test the ability of this model to apply knowledge of indications of prescriptions in Shang Han Lun.Methods This research adapted rules of 30 TCM prescriptions for model use including Guizhi-related prescriptions,Mahuang-related prescriptions,Chaihu-related prescriptions,Dahuang-related prescriptions,Shigao-related prescriptions,Fuzi-related prescriptions,Fuling-related prescriptions and their adjusted prescriptions from Cold Damage and Miscellaneous Diseases.As many as 105 rules were added to the model.The model consists of two parts which are the rule-based case data generator(RCDG)model and the transformer model.The RCDG model yields multiple TCM cases including symptoms,tongue appearance,pulse appearance,and medicines by executing combination,Cartesian product,and montage respectively on TCM rules.All of the generated cases are in accord with the theory of TCM.After that,generated data is passed to a deep learning model Transformer that is based on the encoder-decoder structure to simulate the complex nonlinear mapping from syndromes to corresponding medicines.Results A total of 1,212,795 non-duplicate cases were generated by the RCDG model.5,000 cases were randomly selected as the test set,and the rest were used as the training set or validation set.In the test set,there were 4,983 out of 5,000 cases in which the predicted prescription was the same as the target prescription.The coincidence rate between the predicted prescriptions and the target prescriptions was 99.90%.Conclusion The model can correctly apply the knowledge and simulate preset rules of indications of prescriptions in Shang Han Lun,and can differentiate different syndromes and pathogeneses,which indicates its great development potential in constructing a TCM prescription recommendation system.