SIA OpenIR  > 数字工厂研究室
AttentiveHerb: A Novel Method for Traditional Medicine Prescription Generation
Liu Z(刘智)1,2,3; Zheng ZY(郑泽宇)1,2; Guo XW(郭希旺)4,5; Qi, Liang6; Gui J(桂珺)1,7; Fu DZ(付殿峥)1; Yao, Qingfeng1,2,3; Jin, Luyao8
Department数字工厂研究室
Source PublicationIEEE ACCESS
ISSN2169-3536
2019
Volume7Pages:139069-139085
Indexed BySCI ; EI
EI Accession number20200308053490
WOS IDWOS:000498810100001
Contribution Rank1
Funding OrganizationNational Key Research and Devleopment Program of China [2018YFF0214704] ; Liaoning Province Education Department Scientific Research Foundation of China [L2019027] ; Liaoning Province Dr. Research Foundation of China [20170520135]
KeywordAttention mechanism deep learning neural network sequence learning traditional herbal medicine
Abstract

In this paper, we propose a novel intelligent model, called AttentiveHerb, for simulating the doctor's inquiry and prescription that is composed by a series of herbs. It can automatically simulate some principles and learns the interaction between symptoms and herbs from clinical records of traditional herbal medicine. This model consists of two different attention mechanisms for distinguishing the main symptoms and paying different attention to different symptoms. By experiments, in terms of the predicted prescriptions, 51% of the total cases are in full accordance with the labels; in 1.09% of cases, all herbs of a label can be found in the predicted prescription and the predicted prescription have other additional herbs; in 15.4% of cases, all herbs of a predicted prescription can be found in their corresponding label; in 22.41% of cases, several herbs in each predicted prescription overlap with its label; and 10.1% of cases are completely different from the label. In summary, 67.49% of the predicted prescriptions are close to the labels, and 89.9% contain the same herbs with the labels, which indicates that the prescriptions generated by our model are close to those by doctors. Besides, our model can recommend herbs that do not appear in the label prescriptions but are useful for relieving symptoms. It shows that our model can learn some interactions between herbs and symptoms. With enough normalized traditional herbal medical records, this model works more accurately. This study also provides a benchmark for the upcoming researches in intelligent inquiry and prescription generation of traditional herbal medicine.

Language英语
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS Research AreaComputer Science ; Engineering ; Telecommunications
Funding ProjectNational Key Research and Devleopment Program of China[2018YFF0214704] ; Liaoning Province Education Department Scientific Research Foundation of China[L2019027] ; Liaoning Province Dr. Research Foundation of China[20170520135]
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25981
Collection数字工厂研究室
Corresponding AuthorZheng ZY(郑泽宇); Qi, Liang
Affiliation1.Department of Digital Factory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
5.Computer and Communication Engineering College, Liaoning Shihua University, Fushun 113001, China
6.Department of Intelligent Science and Technology, Shandong University of Science and Technology, Qingdao 266590, China
7.School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China
8.School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Recommended Citation
GB/T 7714
Liu Z,Zheng ZY,Guo XW,et al. AttentiveHerb: A Novel Method for Traditional Medicine Prescription Generation[J]. IEEE ACCESS,2019,7:139069-139085.
APA Liu Z.,Zheng ZY.,Guo XW.,Qi, Liang.,Gui J.,...&Jin, Luyao.(2019).AttentiveHerb: A Novel Method for Traditional Medicine Prescription Generation.IEEE ACCESS,7,139069-139085.
MLA Liu Z,et al."AttentiveHerb: A Novel Method for Traditional Medicine Prescription Generation".IEEE ACCESS 7(2019):139069-139085.
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