Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/60402
Title: A novel approach in discovering significant interactions from TCM patient prescription data
Authors: Poon, SK
Poon, J
Mcgrane, M
Zhou, X
Kwan, P
Zhang, R
Liu, B
Gao, J
Loy, C
Chan, K
Sze, DMY
Keywords: Herb interaction
Complementarities
Traditional Chinese medicine
Super-modularity
Diabetes
Bioinformatics
Patient prescription data
Interacting herbs
Chinese Materia Medica
Pattern mining
Interaction rules mining
Data mining
Issue Date: 2011
Publisher: Inderscience
Source: International journal of data mining and bioinformatics, 2011, v. 5, no. 4, p. 353-368 How to cite?
Journal: International journal of data mining and bioinformatics 
Abstract: The efficacy of a traditional Chinese medicine medication derives from the complex interactions of herbs or Chinese Materia Medica in a formula. The aim of this paper is to propose a new approach to systematically generate combinations of interacting herbs that might lead to good outcome. Our approach was tested on a data set of prescriptions for diabetic patients to verify the effectiveness of detected combinations of herbs. This approach is able to detect effective higher orders of herb-herb interactions with statistical validation. We present an exploratory analysis of clinical records using a pattern mining approach called Interaction Rules Mining.
URI: http://hdl.handle.net/10397/60402
ISSN: 1748-5673 (print)
1748-5681 (online)
DOI: 10.1504/IJDMB.2011.041553
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