Abstract
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.
Highlights
Artificial intelligence is the general term of the modern technology of computer science [1], including image recognition [2], network analysis [3], and natural language processing [4]
We focus on analysing literature and introduce semantic analysis and community detection to analyse the meanings of herbs in the literature to discover core herbs for treating a disease in Traditional Chinese Medicine (TCM)
All articles are related to the TCM treatment of chronic glomerulonephritis (CGN), so we expect semantic analysis can obtain high-quality semantic word vectors of herbs, since a pure in-domain corpus yields better performance than a mixed-domain corpus [71]
Summary
Artificial intelligence is the general term of the modern technology of computer science [1], including image recognition [2], network analysis [3], and natural language processing [4]. The “Sovereign-Minister-Assistant-Courier” combination rule, known as the “Jun-Chen-Zuo-Shi” combination rule, is a major combination principle of TCM formulae [15] According to this principle, the sovereign herb plays a major role for dealing with main symptoms and syndromes of diseases, the minister herb helps the sovereign herb to strengthen herbal efficacy, and the assistant and courier herbs provide supporting function to reconcile formulae (e.g., reducing side effects) [15, 16]. The herbs acting as the sovereign or minister play a key role in terms of treating diseases, while others play an assistant role [16, 17] In this way, the herbs serving as the sovereign or minister are viewed as core herbs in TCM formulae [17,18,19]. Doctors can optimize the herb combination of formulae and synergize herb efficacies to prescribe more effective formulae for treating diseases [15], [19]
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