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

Title
A novel approach in discovering significant interactions from TCM patient prescription data
Publication Date
2011
Author(s)
Poon, Simon
Poon, Josiah
Sze, Daniel
McGrane, Martin
Zhou, Xuezhong
Kwan, Paul H
Zhang, Runshun
Liu, Baoyan
Gao, Junbin
Loy, Clement
Chan, Kelvin
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
Inderscience Publishers
Place of publication
Geneva, Switzerland
DOI
10.1504/IJDMB.2011.041553
UNE publication id
une:9734
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.
Link
Citation
International Journal of Data Mining and Bioinformatics, 5(4), p. 353-368
ISSN
1748-5681
1748-5673
Start page
353
End page
368

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