A novel approach in discovering significant interactions from TCM patient prescription data

Poon, S, Poon, J, McGrane, M, Zhou, X, Kwan, P, Zhang, R, Liu, B, Gao, J, Loy, C, Chan, K and Sze, M 2011, 'A novel approach in discovering significant interactions from TCM patient prescription data', International Journal of Data Mining and Bioinformatics, vol. 5, no. 4, pp. 353-368.


Document type: Journal Article
Collection: Journal Articles

Title A novel approach in discovering significant interactions from TCM patient prescription data
Author(s) Poon, S
Poon, J
McGrane, M
Zhou, X
Kwan, P
Zhang, R
Liu, B
Gao, J
Loy, C
Chan, K
Sze, M
Year 2011
Journal name International Journal of Data Mining and Bioinformatics
Volume number 5
Issue number 4
Start page 353
End page 368
Total pages 16
Publisher Inderscience Enterprises Ltd
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.
Subject Bioinformatics
Keyword(s) Bioinformatics
Complementarities
Diabetes
Interaction
Super-modularity
Traditional Chinese medicine
DOI - identifier 10.1504/IJDMB.2011.041553
Copyright notice © 2011 Inderscience Enterprises Ltd.
ISSN 1748-5673
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Citation counts: TR Web of Science Citation Count  Cited 14 times in Thomson Reuters Web of Science Article | Citations
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