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

Authors: Simon K. Poon, Josiah Poon, Martin McGrane, Xuezhong Zhou, Paul Kwan, Runshun Zhang, Baoyan Liu, Junbin Gao, Clement Loy, Kelvin Chan, Daniel Man-yuen Sze

Addresses: School of Information Technologies, University of Sydney, Sydney, Australia. ' School of Information Technologies, University of Sydney, Sydney, Australia. ' School of Information Technologies, University of Sydney, Sydney, Australia. ' School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China. ' School of Science and Technology, University of New England, Armidale, Australia. ' Guanganmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China. ' China Academy of Chinese Medical Sciences, Beijing, China. ' School of Computing and Mathematics, Charles Sturt University, Australia. ' School of Public Health, University of Sydney, Sydney, Australia. ' Faculty of Pharmacy, University of Sydney, Sydney, Australia. ' School of Information Technologies, University of Sydney, Sydney, Australia; Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong

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.

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.

DOI: 10.1504/IJDMB.2011.041553

International Journal of Data Mining and Bioinformatics, 2011 Vol.5 No.4, pp.353 - 368

Published online: 26 Jul 2011 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article