NDLI: Knowledge Discovery of Selection Rules for Acupuncture Points in Respiratory Diseases Therapy Based on Partial-Ordered Structure Diagrams

Content Provider	IEEE Xplore Digital Library
Author	Lina Hou Zhongpeng Zhang Jialin Song Haibing Zhao Wenxue Hong
Copyright Year	2013
Description	Author affiliation: Coll. of Civil Eng. & Mech., Yanshan Univ., Qinhuangdao, China (Lina Hou) \|\| Dept. of Biomed. Eng., Yanshan Univ., Qinhuangdao, China (Zhongpeng Zhang; Jialin Song; Haibing Zhao; Wenxue Hong)
Abstract	This paper presents a knowledge discovery method of selection rules for acupuncture points in respiratory diseases therapy based on the theory of Structural Partial-Ordered Attribute Diagram and association rule mining. First, we briefly introduced the basic definitions of Structural Partial-Ordered Attribute Diagram and association rule mining theory. Then, we transformed the data of a Traditional Chinese Medicine treatise into formal context and transaction database. Finally, we explained knowledge discovery process by analyzing the formal context of respiratory diseases. It was concluded that the method proposed in this paper works well in discovering new knowledge from medical treatises and clinical cases of acupuncture treatment. The method provided a scientific and advanced technological means for the heritage of Traditional Chinese Medicine.
Sponsorship	IEEE Comput. Soc.
Starting Page	786
Ending Page	790
File Size	221960
Page Count	5
File Format	PDF
ISBN	9780769551227
DOI	10.1109/IMCCC.2013.175
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Association rule mining Databases Medical treatment Respiratory diseases Knowledge Discovery Partial order theory Knowledge discovery Association rules Structural partial-ordered attribute diagram Diseases
Content Type	Text
Resource Type	Article

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