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 |
Appears in Collections: | Journal/Magazine Article |
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