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.

中文翻译：

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一种新型医疗诊断可视化推理智能模型：以中医为例

如何基于已知的诊断知识呈现智能模型来辅助医学诊断并展示推理过程是一个值得探索的有趣问题。本研究以中医案例为基础，开发了一种新颖的医学诊断可视化推理智能模型。选取中医阴虚、肝阴虚、肾阴虚、肝肾阴虚四类作为研究实例。根据《中医诊断学》中的诊断要点知识，针对中医诊断的四类，随机生成2000个样本进行训练和测试。此外，还从医院临床病例中收集了共60份临床样本。将训练样本送入中文Bert预训练语言模型进行训练，生成智能诊断模块。同时，开发了一种数学算法来生成推理图。为了评估模型的性能，计算了准确率、F1分数、Mse、Loss等指标的值，用于模型训练和测试。并绘制混淆矩阵和ROC曲线来估计模型的预测能力。该新颖模型还与 RF 和 XGBOOST 进行了比较。并展示和分析了该模型的推理有向图的一些实例。这可能是解决中医医疗诊断人工智能领域的可解释和推理智能模型问题的一种新尝试。