A novel intelligent model for visualized inference of medical diagnosis: A case of TCM

Abstract

How to present an intelligent model based on known diagnostic knowledge to assist medical diagnosis and display the reasoning process is an interesting issue worth exploring. This study developed a novel intelligent model for visualized inference of medical diagnosis with a case of Traditional Chinese Medicine (TCM). Four classes of TCM's diagnosis composed of Yin deficiency, Liver Yin deficiency, Kidney Yin deficiency, and Liver-Kidney Yin deficiency were selected as research examples. According to the knowledge of diagnostic points in “Diagnostics of TCM”, a total of 2000 samples for training and testing were randomly generated for the four classes of TCM's diagnosis. In addition, a total of 60 clinical samples were collected from hospital clinical cases. Training samples were sent to the pre-training language model of Chinese Bert for training to generate intelligent diagnostic module. Simultaneously, a mathematical algorithm was developed to generate inferential digraphs. In order to evaluate the performance of the model, the values of accuracy, F1 score, Mse, Loss and other indicators were calculated for model training and testing. And the confusion matrices and ROC curves were plotted to estimate the predictive ability of the model. The novel model was also compared with RF and XGBOOST. And some instances of inferential digraphs with the model were displayed and analyzed. It may be a new attempt to solve the problem of interpretable and inferential intelligent models in the field of artificial intelligence on medical diagnosis of TCM.