Poon, Simon; Poon, Josiah; Sze, Daniel; McGrane, Martin; Zhou, Xuezhong; Kwan, Paul H; Zhang, Runshun; Liu, Baoyan; Gao, Junbin; Loy, Clement; Chan, Kelvin

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

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

Publication Date

2011

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.

Citation

International Journal of Data Mining and Bioinformatics, 5(4), p. 353-368

ISSN

1748-5681

1748-5673

Link

https://hdl.handle.net/1959.11/9543

Publisher

Inderscience Publishers

Title

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

Type of document

Journal Article

Entity Type

Publication

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
Publication Date	2011
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.
Citation	International Journal of Data Mining and Bioinformatics, 5(4), p. 353-368
ISSN	1748-5681 1748-5673
Link	https://hdl.handle.net/1959.11/9543
Publisher	Inderscience Publishers
Title	A novel approach in discovering significant interactions from TCM patient prescription data
Type of document	Journal Article
Entity Type	Publication

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

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