• DocumentCode
    3195843
  • Title

    Exploring Li-Fa-Fang-Yao rules of major depressive disorder in traditional Chinese medicine through text mining

  • Author

    Junping Zhan ; Guang Zheng ; Mengmeng Song ; Tong Wei ; Miao Jiang ; Cheng Lu ; Aiping Lu

  • Author_Institution
    Second affiliated Hosp. of Henan, Univ. of traditional Chinese Med., Zhengzhou, China
  • fYear
    2013
  • fDate
    18-21 Dec. 2013
  • Firstpage
    460
  • Lastpage
    464
  • Abstract
    In traditional Chinese medicine, rules of Li-Fa-Fang-Yao is of critical importance in clinical practices. Li-Fa-Fang-Yao, which means principles, methods, formulae, and Chinese herbal medicines respectively, indicate the four basic steps of diagnosis and treatment: determining the cause, mechanism and location of the disease according to the medical theories and principles, then deciding the treatment principle and method, and finally selecting a formula as well as proper Chinese herbal medicines. In this paper, focused on major depressive disorder, we explored the rules of Li-Fa-Fang-Yao within the framework of traditional Chinese medicine. Through calculation, three clusters of Li-Fa-Fang-Yao on major depressive disorder were found based on the syndrome differentiation. What´s more, these three clusters can also be validated by textbooks of traditional Chinese medicine.
  • Keywords
    data mining; diseases; medical diagnostic computing; medical disorders; patient diagnosis; patient treatment; text analysis; Chinese herbal medicines; Li-Fa-Fang-Yao rules; depressive disorder; disease diagnosis; disease treatment; medical theory; syndrome differentiation; text mining; traditional Chinese medicine textbook; Conferences; Diseases; Educational institutions; Liver; Medical diagnostic imaging; Mood; Text mining; Li-Fa-Fang-Yao; depression; text mining; traditional Chinese medicine; validation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on
  • Conference_Location
    Shanghai
  • Type

    conf

  • DOI
    10.1109/BIBM.2013.6732536
  • Filename
    6732536