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 hyperthyroidism, 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 Professor Zhou Zhongying and his team, standardized and structured training data were constructed; algorithms based on attention mechanism 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 stagnation, 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 indicators. 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 method ological references for the inheritance of academic ideas of famous veteran traditional Chinese medicine practitioners and clinical auxiliary diagnosis and treatment decision-making.