• DocumentCode
    612348
  • Title

    Exploring the associating rules of prescription and syndrome on Radix Astragali with text mining

  • Author

    Shuyu Sun ; Changyuan Yu ; Miao Jiang ; Minzhi Wang ; Xiaojuan He ; Aiping Lu ; Guang Zheng ; Aiping Lu

  • Author_Institution
    Coll. of Life Sci. & Technol., Beijing Univ. of Chem. Technol., Beijing, China
  • fYear
    2013
  • fDate
    25-28 May 2013
  • Firstpage
    115
  • Lastpage
    118
  • Abstract
    Single Chinese herbal medicine (CHM) is the basic element in the formulae of traditional Chinese medicine (TCM). How to acquire knowledge of a single CHM within the framework of TCM is a meaningful task. In TCM, Radix Astragali (RA) is frequently and widely used in clinical practice. In order to explore the associating rules between prescription and syndrome on RA with text mining technique, we downloaded the data set on RA from Chinese BioMedical literature database (also called SinoMed). Then, the rules of prescription corresponding to disease and syndrome on RA were mined out by executing data slicing algorithms. The mining results were visually demonstrated with software Cytoscape 2.8. Text mining, together with artificial reading for anti-noising and validation, is an important approach in exploring the rules of prescription corresponding to disease and syndrome. The results showed that RA was usually used in treating diseases of diabetes and cancers. For them, blood stasis due to qi deficiency was the main syndrome in TCM. Moreover, the results demonstrated associations among TCM syndrome, diseases and formulae associated with RA. These associated networks represented a variety of knowledge items which embodied the associating rules between prescription and syndrome on RA.
  • Keywords
    data mining; database management systems; diseases; medical administrative data processing; medicine; text analysis; CHM; Chinese biomedical literature database; Cytoscape 2.8. software; RA; TCM; TCM syndrome; artificial reading; associated networks; associating rules; blood stasis; clinical practice; data slicing algorithms; disease; prescription associating rules; qi deficiency; radix Astragali syndrome; single Chinese herbal medicine; text mining; traditional Chinese medicine; Blood; Databases; Diabetes; Diseases; Educational institutions; Text mining; Data slicing algorithm; Radix Astragali; Text mining; associating rules;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Complex Medical Engineering (CME), 2013 ICME International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4673-2970-5
  • Type

    conf

  • DOI
    10.1109/ICCME.2013.6548222
  • Filename
    6548222