Therapeutic effectiveness evaluation algorithm based on KCCA for neck pain caused by different diagnostic sub-types of cervical spondylosis
there are various measuring tools to evaluate the therapeutic effectiveness of acupuncture for neck pain caused by cervical spondylosis, such as NPQ and MPQ. However, the outcomes are challenged because cervical spondylosis can be sub-divided into different sub-types due to different pathological diagnosis. Therefore, a new algorithm is needed to analyze the difference of therapeutic effectiveness among diagnostic sub-types. We proposed Kernel Canonical Correlation Analysis (KCCA), which has been successfully applied in many statistical learning tasks, as a potential approach to discover the underlying relationship between different effective outcome measures and diagnostic sub-types in clinical practice. The application of kernel mapping on the basis of correlation analysis provides a non-linear relationship expression between input variables. The proposed method is applied to the clinical data from a multi-center randomized controlled trial (RCT) on acupuncture for neck pain caused by cervical spondylosis, and the result shows that it is effective and capable to dramatically improve the correlation between diagnostic sub-types and clinical outcome measures.
cervical spondylosis kernel canonical correlation analysis kernel mapping evaluation of health outcome
Gang Zhang Zhaohui Liang Wenbin Fu Jianhua Liu Jianqiao Fang Nenggui Xu
Faculty of Automation GuangDong University of Technology Guangzhou, China, 510006 Acupuncture Department & Research Team of Acupuncture Effect and Mechanism Guangdong Provincial Hosp The third clinical college Zhejiang Chinese Medical University Hangzhou, China, 310005 Guangzhou University of Chinese Medicine Guangzhou, China, 510405
国际会议
上海
英文
1806-1810
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）