Study of Acupuncture Manipulation Parameter Based on Data Mining Technique

  • Yin-e Hu
  • Hua-yuan Yang
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 19)

Abstract

Currently the scholars study acupuncture manipulation parameter from the two aspect mainly, firstly, they quantify the acupuncture manipulation parameter through clinical work and science experiment method; secondly, acquire the acupuncture manipulation parameter through the crossing of multiply subject, then analyze and deal with the parameters. Due to the analysis and research on acupuncture manipulation parameter is not enough in the earlier work, At the same time, Data Mining has good applications in TCM field. The study explained the science intension of the TCM in modern science knowledge and widened the research idea. The article proposes to acquire with the laboratory apparatus, and mine the rule information from the plenty of the collected acupuncture manipulation parameters with the Data Mining technique. Then find the relation between the acupuncture manipulation parameters and acupuncture curative effect. Consequently it will be make a theory base for the standard, normalization and quantification study of the acupuncture manipulation parameter.

Keywords

Acupuncture manipulation parameter Data Mining Stimulation amount Manipulation waveform Sensor Quantification 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yang huayuan, Gu Xunjie, Xia Jinshan. Development of acupuncture manipulation parameter determination apparatus and analysis of the force of manipulation. Journal of Clinical Acupuncture and Moxibustion. 1995, 11(6): 51Google Scholar
  2. 2.
    Guxing. Development of Teaching Test Apparatus for Acupuncture Manipulations in TCM. Chinese acupuncture & Moxibustion. 2001; 21(4):229Google Scholar
  3. 3.
    Ding Guanghong, Shen Xueyong, Dai Jianhua, etc. Research and Development on the Dynamic System for Detecting the Force of Acupuncture Needle during the Acupuncture Process in the Clinical Practice of Traditional Chinese Medicine. Journal of Biomedical Engineering. 2003, 20(1):121–124Google Scholar
  4. 4.
    Li Qinghua, Li Fuguo, Ai Bingwei. Research of Detecting Acupuncture Needle for Acupuncture Manipulation. Chinese Journal of Sensors and Actuators. 2006,19(2) 285–288.Google Scholar
  5. 5.
    Liu Tangyi, Yang Huayuan, Gu Xunjie, Xia Jinshan. Development of model ATP-I acupuncture manipulation parameter determination apparatus. Chinese Acupuncture & Moxibustion. 2003,23(11): 668–670Google Scholar
  6. 6.
    Yang Huayuan, Gu Xunjie, Xia Jinshan. Development and application of acupuncture manipulation parameter analysis apparatus. Shanghai Journal of Acupuncture and Moxibustion. 1991, 3:35–36Google Scholar
  7. 7.
    Ding Guanghong, Shen Xueyong, etc. THE CONTRAST RESEARCH BETWEEN ACUPUNCTURE MANIPULATIONS AND THE PARAMETERS OF FORCE ACTING ON NEEDLE. Chinese Journal of Biomedical Engineering. 2004, 23(4):254–256Google Scholar
  8. 8.
    Hua-Yuan YANG, Tang-Yi LIU, Le KUAI, Ming GAO, Zhui-Cheng HU. Research on real-time data collection and demonstration system for acupuncture manipulation. Journal of Chinese Integrative Medicine. 2006, 5(4):311–314.CrossRefGoogle Scholar
  9. 9.
    Shi Xuemin. Issues and Countermeasure Lie Ahead of Acupuncture and Moxibustion in the New Century. Traditional Chinese Medicine Journal. 2002,1(3):1–4.Google Scholar
  10. 10.
    Fayyad U, Shapiro GP, Smyth P. From Data Mining to Knowledge Discovery: An Overview. In: Fayyad (Eds). Advances in Knowledge Discovery and Data Mining. Cambridge, MA: MIT Press, 1996:1–3Google Scholar
  11. 11.
    Qiao Yanjiang, Li Pengtao, Su Ganqiang. The meaning of KDD study on traditional Chinese compound prescription. Journal of Beijing University of Traditional Chinese Medicine. 1998,21(3):15–17.Google Scholar
  12. 12.
    Qin Xue-jun, SHI Cheng. The application of data mining in the area of traditional Chinese Medicine. Medical Information. 2006,19(5):945–947Google Scholar
  13. 13.
    He Qianfeng, Cui Meng, Wu Chaohui. The Knowledge Discovery of Formula Construction. Journal of the China Society for Scientific and Technical Information. 2004,11(7):655–658.Google Scholar
  14. 14.
    Yao Meicun, Ai Lu, Yuan Yuemei. Analysis of the Association Rule in the Composition of the TCM Formulas for Diabetes. Journal of Beijing University of Traditional Chinese Medicine. 2002,25(6):48–50.Google Scholar
  15. 15.
    Song Yaoping, Li Kun, Wu Mengxu. Research to Association of Couplet Medicines Dosage. Liaoning Journal of Traditional Chinese Medicine. 2006,33(9):1095–1096Google Scholar
  16. 16.
    Wang Xuewei, quhaibin, Wang Jie. A quantitative diagnostic method based on data-mining approach in TCM. Journal of Beijing University of Traditional Chinese Medicine. 2005,28(1): 4–7Google Scholar
  17. 17.
    Liu Baoyan, He Liyun, Xie YanMing. The logistic regression method based on the data mining process-The application of the sub-health classification and the analysis of effect factors. World Science and Technology-Modernization of Traditional Chinese Medicine. 2006,8(2):48–52Google Scholar
  18. 18.
    Liang Weixiong, Wen Zhehuai, Ou Aihua, etc. Multivariate Analysis of TCM Syndromes of Apoplex at Acute Stage. Journal of GuangZhou University of Traditional Chinese Medicine. 1998, 15(4):293Google Scholar
  19. 19.
    Zhang Shijun, Shen Mingxiu, Wang Xianchun, etc. Cluster Analysis of Variables in Liver Syndrome of TCM. Chinese Journal of Integrated Traditional and Western Medicine. 2004,24(1):75–76Google Scholar
  20. 20.
    Yuan Shi-hong, Wang Mi-qu, Wang Tian-fang, etc. Study on kidney-deficiency symptoms with cluster analysis. Journal of Beijing University of Traditional Chinese Medicine. 2006,29(4):254–257.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yin-e Hu
    • 1
  • Hua-yuan Yang
    • 1
  1. 1.Shanghai University of TCMShanghaiChina

Personalised recommendations