School of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China.
School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, 350118, China.
J Ethnopharmacol. 2022 Oct 28;297:115109. doi: 10.1016/j.jep.2022.115109. Epub 2022 Feb 25.
The recommendation of herbal prescriptions is a focus of research in traditional Chinese medicine (TCM). Artificial intelligence (AI) algorithms can generate prescriptions by analysing symptom data. Current models mainly focus on the binary relationships between a group of symptoms and a group of TCM herbs. A smaller number of existing models focus on the ternary relationships between TCM symptoms, syndrome-types and herbs. However, the process of TCM diagnosis (symptom analysis) and treatment (prescription) is, in essence, a "multi-ary" (n-ary) relationship. Present models fall short of considering the n-ary relationships between symptoms, state-elements, syndrome-types and herbs. Therefore, there is room for improvement in TCM herbal prescription recommendation models.
To portray the n-ary relationship, this study proposes a prescription recommendation model based on a multigraph convolutional network (MGCN). It introduces two essential components of the TCM diagnosis process: state-elements and syndrome-types.
The MGCN consists of two modules: a TCM feature-aggregation module and a herbal medicine prediction module. The TCM feature-aggregation module simulates the n-ary relationships between symptoms and prescriptions by constructing a symptom-'state element'-symptom graph (S) and a symptom-'syndrome-type'-symptom graph (T). The herbal medicine prediction module inputs state-elements, syndrome-types and symptom data and uses a multilayer perceptron (MLP) to predict a corresponding herbal prescription. To verify the effectiveness of the proposed model, numerous quantitative and qualitative experiments were conducted on the Treatise on Febrile Diseases dataset.
In the experiments, the MGCN outperformed three other algorithms used for comparison. In addition, the experimental data shows that, of these three algorithms, the SVM performed best. The MGCN was 4.51%, 6.45% and 5.31% higher in Precision@5, Recall@5 and F1-score@5, respectively, than the SVM. We set the K-value to 5 and conducted two qualitative experiments. In the first case, all five herbs in the label were correctly predicted by the MGCN. In the second case, four of the five herbs were correctly predicted.
Compared with existing AI algorithms, the MGCN significantly improved the accuracy of TCM herbal prescription recommendations. In addition, the MGCN provides a more accurate TCM prescription herbal recommendation scheme, giving it great practical application value.
草药处方的推荐是传统中医（TCM）研究的重点。人工智能（AI）算法可以通过分析症状数据来生成处方。目前的模型主要侧重于一组症状和一组 TCM 草药之间的二元关系。少数现有的模型侧重于 TCM 症状、症型和草药之间的三元关系。然而，TCM 诊断（症状分析）和治疗（处方）的过程本质上是一种“多进制”（n 进制）关系。目前的模型未能考虑症状、状态元素、症型和草药之间的 n 进制关系。因此，TCM 草药处方推荐模型还有改进的空间。
为了描绘 n 进制关系，本研究提出了一种基于多图卷积网络（MGCN）的处方推荐模型。它引入了 TCM 诊断过程的两个基本组成部分：状态元素和症型。
MGCN 由两个模块组成：TCM 特征聚合模块和草药预测模块。TCM 特征聚合模块通过构建症状-状态元素-症状图（S）和症状-症型-症状图（T）来模拟症状和处方之间的 n 进制关系。草药预测模块输入状态元素、症型和症状数据，并使用多层感知机（MLP）预测相应的草药处方。为了验证所提出模型的有效性，在《伤寒论》数据集上进行了大量定量和定性实验。
在实验中，MGCN 优于其他三种用于比较的算法。此外，实验数据表明，在这三种算法中，SVM 的性能最好。MGCN 在 Precision@5、Recall@5 和 F1-score@5 上分别比 SVM 高 4.51%、6.45%和 5.31%。我们将 K 值设置为 5，并进行了两个定性实验。在第一种情况下，MGCN 正确预测了标签中的所有五种草药。在第二种情况下，正确预测了五种草药中的四种。
与现有的 AI 算法相比，MGCN 显著提高了 TCM 草药处方推荐的准确性。此外，MGCN 提供了更准确的 TCM 处方草药推荐方案，具有很大的实际应用价值。
