THCluster: herb supplements categorization for precision traditional Chinese medicine

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...

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Main Authors: Ruan, Chunyang, Wang, Ye, Zhang, Yanchun, Ma, Jiangang, Chen, Huijuan, Aickelin, Uwe, Zhu, Shanfeng, Zhang, Ting
Format: Conference or Workshop Item
Published: 2017
Subjects:
Online Access:https://eprints.nottingham.ac.uk/48067/
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author Ruan, Chunyang
Wang, Ye
Zhang, Yanchun
Ma, Jiangang
Chen, Huijuan
Aickelin, Uwe
Zhu, Shanfeng
Zhang, Ting
author_facet Ruan, Chunyang
Wang, Ye
Zhang, Yanchun
Ma, Jiangang
Chen, Huijuan
Aickelin, Uwe
Zhu, Shanfeng
Zhang, Ting
author_sort Ruan, Chunyang
building Nottingham Research Data Repository
collection Online Access
description 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.
first_indexed 2025-11-14T20:07:46Z
format Conference or Workshop Item
id nottingham-48067
institution University of Nottingham Malaysia Campus
institution_category Local University
last_indexed 2025-11-14T20:07:46Z
publishDate 2017
recordtype eprints
repository_type Digital Repository
spelling nottingham-480672020-05-04T19:17:37Z https://eprints.nottingham.ac.uk/48067/ 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 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. 2017-11-13 Conference or Workshop Item PeerReviewed Ruan, Chunyang, Wang, Ye, Zhang, Yanchun, Ma, Jiangang, Chen, Huijuan, Aickelin, Uwe, Zhu, Shanfeng and Zhang, Ting (2017) THCluster: herb supplements categorization for precision traditional Chinese medicine. In: IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2017), 13-16 Nov 2017, Kansas City, Mo., USA. Herb categorization Heterogeneous information network Clustering http://ieeexplore.ieee.org/abstract/document/8217685/
spellingShingle Herb categorization
Heterogeneous information network
Clustering
Ruan, Chunyang
Wang, Ye
Zhang, Yanchun
Ma, Jiangang
Chen, Huijuan
Aickelin, Uwe
Zhu, Shanfeng
Zhang, Ting
THCluster: herb supplements categorization for precision traditional Chinese medicine
title THCluster: herb supplements categorization for precision traditional Chinese medicine
title_full THCluster: herb supplements categorization for precision traditional Chinese medicine
title_fullStr THCluster: herb supplements categorization for precision traditional Chinese medicine
title_full_unstemmed THCluster: herb supplements categorization for precision traditional Chinese medicine
title_short THCluster: herb supplements categorization for precision traditional Chinese medicine
title_sort thcluster: herb supplements categorization for precision traditional chinese medicine
topic Herb categorization
Heterogeneous information network
Clustering
url https://eprints.nottingham.ac.uk/48067/
https://eprints.nottingham.ac.uk/48067/