School of Informatics, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, China.
School of Informatics, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, China; Big Data Analysis Laboratory of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, China.
J Ethnopharmacol. 2023 Oct 28;315:116693. doi: 10.1016/j.jep.2023.116693. Epub 2023 May 30.
Traditional Chinese Medicine (TCM) prescriptions are a product of the Chinese medical theory's distinct thinking and clinical experience. TCM practitioners treat diseases by enhancing the efficacy of TCM prescriptions and reducing their poisonous effects. Some TCM herb recommendation methods have been provided for curing the given symptoms to generate a group of herbs according to the TCM principles. However, they ignored the symptoms' semantic characteristics and herbs' different effects on symptoms.
We aim to recommend TCM herbs by considering symptoms' semantic information and the strength of different herbs in curing symptoms.
We propose a herb recommendation model named Multi-Graph Residual Attention Network and Semantic Knowledge Fusion (SMRGAT) to address these problems. Concretely, it uses a multi-head attention mechanism to focus on herbs' different effects on symptoms. Meanwhile, it augments entities' features with a residual network structure while incorporating symptoms' semantic information and external knowledge of herbs. We will verify the effect of SMRGAT on the existing public datasets and the datasets that we have collected and cleaned.
Compared with the current best TCM herb recommendation model, on the public dataset, SMRGAT were increased by 15.11%, 20.60%, and 18.25% in Precision@5, Recall@5, and F1 - score@5, respectively; on ours, respectively increased by 9.72%, 9.03%, 9.24%.
Our experimental results on two datasets indicate that SMRGAT is capable of recommending herbs with greater precision and outperforms several comparison methods. It can provide a basis for assisting TCM clinical prescriptions.
中药（TCM）处方是中医理论独特思维和临床经验的产物。中医从业者通过增强 TCM 处方的疗效和降低其毒性作用来治疗疾病。一些中医草药推荐方法已经提供了针对给定症状的治疗方法，根据中医原则生成了一组草药。然而，它们忽略了症状的语义特征和草药对症状的不同影响。
我们旨在通过考虑症状的语义信息和不同草药治疗症状的强度来推荐 TCM 草药。
我们提出了一种名为多图残差注意力网络和语义知识融合（SMRGAT）的草药推荐模型来解决这些问题。具体来说，它使用多头注意力机制来关注草药对症状的不同影响。同时，它通过残差网络结构增强实体的特征，同时合并症状的语义信息和草药的外部知识。我们将验证 SMRGAT 在现有公共数据集和我们收集和清理的数据集上的效果。
与当前最佳的 TCM 草药推荐模型相比，在公共数据集上，SMRGAT 在 Precision@5、Recall@5 和 F1@5 上分别提高了 15.11%、20.60%和 18.25%；在我们的数据集上，分别提高了 9.72%、9.03%和 9.24%。
我们在两个数据集上的实验结果表明，SMRGAT 能够更准确地推荐草药，并且优于几种比较方法。它可以为辅助 TCM 临床处方提供依据。
