Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/9543
Title: | A novel approach in discovering significant interactions from TCM patient prescription data | Contributor(s): | Poon, Simon (author); Poon, Josiah (author); Sze, Daniel (author); McGrane, Martin (author); Zhou, Xuezhong (author); Kwan, Paul H (author); Zhang, Runshun (author); Liu, Baoyan (author); Gao, Junbin (author); Loy, Clement (author); Chan, Kelvin (author) | Publication Date: | 2011 | DOI: | 10.1504/IJDMB.2011.041553 | Handle Link: | https://hdl.handle.net/1959.11/9543 | 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. | Publication Type: | Journal Article | Source of Publication: | International Journal of Data Mining and Bioinformatics, 5(4), p. 353-368 | Publisher: | Inderscience Publishers | Place of Publication: | Geneva, Switzerland | ISSN: | 1748-5681 1748-5673 |
Fields of Research (FoR) 2008: | 010402 Biostatistics 080109 Pattern Recognition and Data Mining 110404 Traditional Chinese Medicine and Treatments |
Socio-Economic Objective (SEO) 2008: | 920203 Diagnostic Methods 890201 Application Software Packages (excl. Computer Games) 970111 Expanding Knowledge in the Medical and Health Sciences 920104 Diabetes |
Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
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Appears in Collections: | Journal Article |
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