Abstract
Traditional Chinese medicine (TCM) is an interesting research topic in China’s thousands of years of history. With the recent advances in artificial intelligence technology, some researchers have started to focus on learning the TCM prescriptions in a data-driven manner. This involves appropriately recommending a set of herbs based on patients’ symptoms. Most existing herb recommendation models disregard TCM domain knowledge, for example, the interactions between symptoms and herbs and the TCM-informed observations (i.e., TCM formulation of prescriptions). In this paper, we propose a knowledge-guided and TCM-informed approach for herb recommendation. The knowledge used includes path interactions and co-occurrence relationships among symptoms and herbs from a knowledge graph generated from TCM literature and prescriptions. The aforementioned knowledge is used to obtain the discriminative feature vectors of symptoms and herbs via a graph attention network. To increase the ability of herb prediction for the given symptoms, we introduce TCM-informed observations in the prediction layer. We apply our proposed model on a TCM prescription dataset, demonstrating significant improvements over state-of-the-art herb recommendation methods.
摘要
在中国几千年历史中, 中医一直是人们关注的焦点. 近年来, 随着人工智能技术的兴起, 部分研究开始以数据驱动的方式学习中医的方剂, 即根据病人的症状推荐一组药材. 现有大多数药材推荐模型忽略了中医领域的知识, 例如药材和症状之间的关系, 中药药方形成逻辑, 等等. 本文提出一种以知识为引导、 结合中医学信息的药材推荐方法. 本文使用的知识包括从中医典籍及处方中提取的知识图谱, 以此得到症状和药材之间的交互和共生关系. 利用这些信息, 基于图注意力网络提取症状和药材的特征向量. 在此基础上, 将处方学等中医学信息加入到预测层中, 提高了模型对药材的预测能力. 最后, 在中医处方数据集上进行的实验表明, 该方法优于目前主流的药材推荐算法.
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Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Zhe JIN and Yunhe PAN designed the research. Zhe JIN and Jiaxu MIAO processed the data and drafted the paper. Yin ZHANG helped organize the paper. Yi YANG, Yueting ZHUANG, and Yin ZHANG revised and finalized the paper.
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Yi YANG, Yueting ZHUANG, and Yunhe PAN are editorial board members for Frontiers of Information Technology & Electronic Engineering, and they were not involved with the peer review process of this paper. All the authors declare that they have no conflict of interest.
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Project supported by the China Knowledge Centre for Engineering Sciences and Technology (CKCEST) and the National Natural Science Foundation of China (No. 62037001)
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Jin, Z., Zhang, Y., Miao, J. et al. A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation. Front Inform Technol Electron Eng 24, 1416–1429 (2023). https://doi.org/10.1631/FITEE.2200662
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DOI: https://doi.org/10.1631/FITEE.2200662