Introduction

Traditional Chinese Medicine (TCM) diagnosis is a reasoning process through expert knowledge, in which syndrome classification is a key step for prescription recommendation and the treatment of patients. Doctors generally differentiate syndrome types according to patients’ symptoms and state elements. This paper proposes a syndrome classification method based on graph convolutional network with residual structure, to exploit the potential relationship between symptoms and state elements.

Methods

We constructed a graph convolutional network by combining symptoms and state elements for syndrome classification, called Symptoms-State elements Graph Convolutional Network (SSGCN), embedding the inherent logic of TCM diagnosis and treatment with a prescription graph. This graph architecture wherein contained the relationship between symptoms and state elements, and a multi-layer perceptron (MLP) was trained to classify different syndromes.

Results

Experiments were conducted on two self-built datasets according to two classic TCM books, i.e., Theories on Febrile Diseases and Traditional Chinese Medicine Prescription Dictionary. Accuracy, precision, recall and F1-score were adopted to evaluate the syndrome classificaiton results. Our proposed SSGCN method achieved accuracy of 75.59%, 69.63%, precision of 69.10%, 76.33%, recall of 75.63%, 66.67% and F1-score of 71.26%, 65.84% in the above two datasets, respectively. The proposed method for syndrome classification outperformed several popular methods including support vector machine, random forest, extreme gradient boosting and convolutional neural network.

Conclusions

By constructing a prescription graph in which symptoms are used as nodes and state elements are taken into account for edges, graph convolution is implemted to capture the relationship of symptoms and state elements. This model improves the performance of syndrome classification and can be further extened for some other related applications in TCM.

中文翻译：

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使用图卷积网络进行中医证候分类

介绍

中医诊断是通过专家知识进行推理的过程，其中证候分类是处方推荐和患者治疗的关键步骤。医生一般根据患者的症状和状态要素来辨证。本文提出了一种基于残差结构图卷积网络的症状分类方法，以挖掘症状与状态要素之间的潜在关系。

方法

我们通过结合症状和状态要素进行证候分类，构建了一个图卷积网络，称为症状-状态要素图卷积网络（SSGCN），将中医诊断和治疗的内在逻辑嵌入处方图。该图架构包含症状和状态元素之间的关系，并且训练多层感知器（MLP）来对不同的综合症进行分类。

结果

根据《伤寒论》和《中药方剂词典》两本经典中医书籍，在自建的两个数据集上进行实验。采用准确率、精确率、召回率和F1分数来评价证候分类结果。我们提出的SSGCN方法在上述两个数据集中分别实现了75.59%、69.63%的准确率、69.10%、76.33%的精确率、75.63%、66.67%的召回率和71.26%、65.84%的F1分数。所提出的综合症分类方法优于几种流行的方法，包括支持向量机、随机森林、极限梯度提升和卷积神经网络。

结论

通过构建以症状为节点、以状态元素为边的处方图，实现图卷积来捕获症状和状态元素的关系。该模型提高了证候分类的性能，并且可以进一步扩展到中医中的一些其他相关应用。