THCluster: herb supplements categorization for precision traditional Chinese medicine

Chunyang Ruan, Ye Wang, Yanchun Zhang, Jiangang Ma, Huijuan Chen, Uwe Aickelin, Shanfeng Zhu, Ting Zhang

Research output: Contribution to conferencePaper

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Abstract

There has been a continuing demand for traditional and complementary medicine worldwide. A fundamental and important topic in Traditional Chinese Medicine (TCM) is to optimize the prescription and to detect herb regularities from TCM data. In this paper, we propose a novel clustering model to solve this general problem of herb categorization, a pivotal task of prescription optimization and herb regularities. The model utilizes Random Walks method, Bayesian rules and Expectation Maximization (EM) models to complete a clustering analysis effectively on a heterogeneous information network. We performed extensive experiments on the real-world datasets and compared our method with other algorithms and experts. Experimental results have demonstrated the effectiveness of the proposed model for discovering useful categorization of herbs and its potential clinical manifestations.
Original languageEnglish
Publication statusPublished - 13 Nov 2017
EventIEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2017) - Kansas City, Mo., USA
Duration: 13 Nov 201716 Nov 2017

Conference

ConferenceIEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2017)
Period13/11/1716/11/17

Keywords

  • Herb categorization, Heterogeneous information network, Clustering

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