Paper Info
Title
A kernel-decision tree based algorithm for outcome prediction on acupuncture for neck pain: A new method for interim analysis
Abstract
Neck pain is a common disorder in modern society as the result of changes in working and life style. Acupuncture is a traditional treatment of Chinese medicine for neck pain, whose therapeutic mechanism follows the classic knowledge and understanding of Chinese medicine. Syndrome-based diagnosis and treatment is a significant feature of Chinese medicine, and guides the practice of acupuncture. In the treatment of neck pain, acupuncture provides a standard prescription whose effect is support by latest multi-center RCTs. However, the potential difference of its effectiveness in different syndrome types is challenged due to small sample size and limits of statistical power. In our study, we apply the machine learning methods to a data set of the outcomes of a multi-center RCT clinical trial, which consists of demographical information and efficacy outcomes. A decision tree with kernel mapping was applied as the main algorithm to discover the underlying relationship and difference between clinical outcomes among different syndrome types, and to predict its tendency in trials with larger sample size. Kernel function is used to map the input data items to a feature space with better representation, which yields a smooth KNN classification boundary. Non-Dominated Sort (NDS) is used to obtain an optimal order of the three efficacy outcomes from a small sample at the beginning. Then the proposed method was tested with the clinical data from a large sample from a multi-center RCT conducted from 2006 to 2010. The result shows the proposed algorithm is capable of discovering the underlying difference among different syndrome types and feasible to predict the effective tendency in clinical trials of large sample. Therefore, it provides a potential solution for interim analysis of clinical trials, which overcomes the limitation of conventional statistical methods.
Year
DOI
Venue
2011
10.1109/BIBMW.2011.6112467
BIBM Workshops
Keywords
Field
DocType
clinical data,neck pain,clinical trial,medical disorders,machine learning method,kernel-decision tree,multi-center rct clinical trial,learning (artificial intelligence),efficacy outcome,large sample,smooth knn classification boundary,demographical information,acupuncture,clinical outcome,different syndrome type,larger sample size,multicenter rct clinical trial,medical computing,kernel mapping,outcome prediction,chinese medicine,decision tree,non-dominated sort,kernel-decision tree based algorithm,interim analysis,patient treatment,new method,potential difference,kernel function,sample size,learning artificial intelligence,statistical power,feature space,machine learning
Data mining,Decision tree,Computer science,Clinical trial,Randomized controlled trial,Artificial intelligence,Statistical power,Interim analysis,Neck pain,Algorithm,Acupuncture,Machine learning,Sample size determination
Conference
ISSN
ISBN
Citations 
2163-6966
978-1-4577-1612-6
1
PageRank 
References 
Authors
0.48
7
7
Name
Order
Citations
PageRank
Zhaohui Liang12615.31
Gang Zhang230.84
Shujun Xu311.50
Ai-hua Ou413.86
Jianqiao Fang531.33
Nenggui Xu631.33
Wenbin Fu799.14