Paper Info
Title
A similarity based learning framework for interim analysis of outcome prediction of acupuncture for neck pain.
Abstract
Chronic neck pain is a common morbid disorder in modern society. Acupuncture has been administered for treating chronic pain as an alternative therapy for a long time, with its effectiveness supported by the latest clinical evidence. However, the potential effective difference in different syndrome types is questioned due to the limits of sample size and statistical methods. We applied machine learning methods in an attempt to solve this problem. Through a multi-objective sorting of subjective measurements, outstanding samples are selected to form the base of our kernel-oriented model. With calculation of similarities between the concerned sample and base samples, we are able to make full use of information contained in the known samples, which is especially effective in the case of a small sample set. To tackle the parameters selection problem in similarity learning, we propose an ensemble version of slightly different parameter setting to obtain stronger learning. The experimental result on a real data set shows that compared to some previous well-known methods, the proposed algorithm is capable of discovering the underlying difference among different syndrome types and is feasible for predicting the effective tendency in clinical trials of large samples.
Year
DOI
Venue
2013
10.1504/IJDMB.2013.056643
IJDMB
Keywords
DocType
Volume
known sample,neck pain,concerned sample,base sample,large sample,outstanding sample,different syndrome type,different parameter,sample size,effective tendency,potential effective difference,outcome prediction,interim analysis
Journal
8
Issue
ISSN
Citations 
4
1748-5673
4
PageRank 
References 
Authors
0.54
14
5
Name
Order
Citations
PageRank
Gang Zhang140.54
Zhaohui Liang22615.31
Jian Yin31056.71
Wenbin Fu499.14
Guo-Zheng Li536842.62