Expansion Design and Experimental Study on Knowledge Base of the Therapeutic Model for Treatment with Prescriptions of Traditional Chinese Medicine
10.3969/j.issn.1005-5304.2014.09.004
- VernacularTitle:方剂治法模型知识库的扩展设计和建模实验研究
- Author:
Fan ZHANG
;
Tingge REN
;
Quanquan GAO
;
Xiaofeng LIU
;
Yan SUN
;
Yongyi CHEN
;
Pengna ZHAO
- Publication Type:Journal Article
- Keywords:
TCM prescription;
knowledge discovery;
machine learning
- From:
Chinese Journal of Information on Traditional Chinese Medicine
2014;(9):13-16
- CountryChina
- Language:Chinese
-
Abstract:
Objective To perfect the prescription knowledge discovery methods; To discover the key factors affecting the robustness of prescription therapeutic model as well as improve its recognition capability.Methods Expanded knowledge base and improved design of Chinese Medicine Prescriptions Intelligence Analytic System (CPIAS) were proposed, such as the establishment of the heuristic filtering rules of efficacy-syndrome relationship, knowledge table of efficacy-syndrome element relationship, identification of efficacy-syndrome element relationship, and syndrome element-syndrome relationship. In addition, quantitative data were calculated by CPIAS. Prescription therapeutic modeling experiments on the Chinese medicine prescriptions system were conducted based on support vector machine (CPSVM), which was also used to analyze the learning outcomes.Results Using expanded knowledge base and improved calculation results can significantly promote learning abilities of CPSVM.Conclusion Screening of efficacies, sorting of symptoms, and collection of syndrome elements are the key factors affecting the quality of prescription therapeutic model.
