A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study
Joint Authors
Zhai, Shuangqing
Liu, Yongguo
Zhu, Jiajing
Jin, Rongjiang
Wen, Chuanbiao
Zhang, Yun
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-23, 23 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-01
Country of Publication
Egypt
No. of Pages
23
Main Subjects
Abstract EN
The Traditional Chinese Medicine (TCM) formula is the main treatment method of TCM.
A formula often contains multiple herbs where core herbs play a critical therapeutic effect for treating diseases.
It is of great significance to find out the core herbs in formulae for providing evidences and references for the clinical application of Chinese herbs and formulae.
In this paper, we propose a core herb discovery model CHDSC based on semantic analysis and community detection to discover the core herbs for treating a certain disease from large-scale literature, which includes three stages: corpus construction, herb network establishment, and core herb discovery.
In CHDSC, two artificial intelligence modules are used, where the Chinese word embedding algorithm ESSP2VEC is designed to analyse the semantics of herbs in Chinese literature based on the stroke, structure, and pinyin features of Chinese characters, and the label propagation-based algorithm LILPA is adopted to detect herb communities and core herbs in the herbal semantic network constructed from large-scale literature.
To validate the proposed model, we choose chronic glomerulonephritis (CGN) as an example, search 1126 articles about how to treat CGN in TCM from the China National Knowledge Infrastructure (CNKI), and apply CHDSC to analyse the collected literature.
Experimental results reveal that CHDSC discovers three major herb communities and eighteen core herbs for treating different CGN syndromes with high accuracy.
The community size, degree, and closeness centrality distributions of the herb network are analysed to mine the laws of core herbs.
As a result, we can observe that core herbs mainly exist in the communities with more than 25 herbs.
The degree and closeness centrality of core herb nodes concentrate on the range of [15, 40] and [0.25, 0.45], respectively.
Thus, semantic analysis and community detection are helpful for mining effective core herbs for treating a certain disease from large-scale literature.
American Psychological Association (APA)
Zhang, Yun& Liu, Yongguo& Zhu, Jiajing& Zhai, Shuangqing& Jin, Rongjiang& Wen, Chuanbiao. 2020. A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-23.
https://search.emarefa.net/detail/BIM-1139354
Modern Language Association (MLA)
Zhang, Yun…[et al.]. A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-23.
https://search.emarefa.net/detail/BIM-1139354
American Medical Association (AMA)
Zhang, Yun& Liu, Yongguo& Zhu, Jiajing& Zhai, Shuangqing& Jin, Rongjiang& Wen, Chuanbiao. A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-23.
https://search.emarefa.net/detail/BIM-1139354
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139354