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: 1376
Citations: Bibtex RefMan EndNote GB/T7714
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
ER -
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.
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