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
Link To Document