THCluster: herb supplements categorization for precision traditional Chinese medicine

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

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.

Citation

Ruan, C., Wang, Y., Zhang, Y., Ma, J., Chen, H., Aickelin, U., …Zhang, T. (2017). THCluster: herb supplements categorization for precision traditional Chinese medicine.

• Conference Name: IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2017)
• End Date: Nov 16, 2017
• Acceptance Date: Nov 9, 2017
• Online Publication Date: Dec 18, 2017
• Publication Date: Nov 13, 2017
• Deposit Date: Nov 14, 2017
• Publicly Available Date: Nov 14, 2017
• Journal: 2017 IEEE International Conference on Bioinformatics and Biomedicine, Kansas City, MO, USA
• Peer Reviewed: Peer Reviewed
• Keywords: Herb categorization, Heterogeneous information network, Clustering
• Public URL: https://nottingham-repository.worktribe.com/output/895039
• Publisher URL: http://ieeexplore.ieee.org/abstract/document/8217685/
• Additional Information: Published in: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Piscataway, N.J. : IEEE, c2017. Electronic ISBN: 978-1-5090-3050-7 pp. 417-424, doi:10.1109/BIBM.2017.8217685 © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.