Full Text:   <491>

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CLC number: TP391

On-line Access: 2023-10-27

Received: 2022-10-25

Revision Accepted: 2023-10-27

Crosschecked: 2023-02-28

Cited: 0

Clicked: 581

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yin Zhang

https://orcid.org/0000-0001-6986-4227

Zhe JIN

https://orcid.org/0000-0002-4221-4547

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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.10 P.1416-1429

http://doi.org/10.1631/FITEE.2200662


A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation


Author(s):  Zhe JIN, Yin ZHANG, Jiaxu MIAO, Yi YANG, Yueting ZHUANG, Yunhe PAN

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   11521043@zju.edu.cn, yinzh@zju.edu.cn

Key Words:  Traditional Chinese medicine, Herb recommendation, Knowledge graph, Graph attention network


Zhe JIN, Yin ZHANG, Jiaxu MIAO, Yi YANG, Yueting ZHUANG, Yunhe PAN. A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(10): 1416-1429.

@article{title="A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation",
author="Zhe JIN, Yin ZHANG, Jiaxu MIAO, Yi YANG, Yueting ZHUANG, Yunhe PAN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="10",
pages="1416-1429",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200662"
}

%0 Journal Article
%T A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation
%A Zhe JIN
%A Yin ZHANG
%A Jiaxu MIAO
%A Yi YANG
%A Yueting ZHUANG
%A Yunhe PAN
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 10
%P 1416-1429
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200662

TY - JOUR
T1 - A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation
A1 - Zhe JIN
A1 - Yin ZHANG
A1 - Jiaxu MIAO
A1 - Yi YANG
A1 - Yueting ZHUANG
A1 - Yunhe PAN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 10
SP - 1416
EP - 1429
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2200662


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.

一种知识引导的基于中医学信息的药材推荐方法

金哲,张引,苗嘉旭,杨易,庄越挺,潘云鹤
浙江大学计算机科学与技术学院,中国杭州市,310027
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。

关键词:中医;药材推荐;知识图谱;图注意力网络

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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