TY - CONF
T1 - THCluster: herb supplements categorization for precision traditional Chinese medicine
AU - Ruan, Chunyang
AU - Wang, Ye
AU - Zhang, Yanchun
AU - Ma, Jiangang
AU - Chen, Huijuan
AU - Aickelin, Uwe
AU - Zhu, Shanfeng
AU - Zhang, Ting
N1 - Note: 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.
PY - 2017/11/13
Y1 - 2017/11/13
N2 - 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.
AB - 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.
KW - Herb categorization, Heterogeneous information network, Clustering
KW - Herb categorization, Heterogeneous information network, Clustering
M3 - Paper
T2 - IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2017)
Y2 - 13 November 2017 through 16 November 2017
ER -