Conference Paper

A kernel-decision tree based algorithm for outcome prediction on acupuncture for neck pain: A new method for interim analysis

DOI: 10.1109/BIBMW.2011.6112467 Conference: 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), Atlanta, GA, USA, November 12-15, 2011
Source: DBLP


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.

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