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
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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ABSTRACT: Comparative effectiveness research (CER) is a new clinical study model featured by its strategic framework consists of four categories and three themes. The core strategy of CER is to conduct observational longitude research supported by electronic registry and large database based on real world practice. Since CER studies do not uses a classic randomized control trial (RCT) design, the well-developed data analytic methods for RCTs are challenged. The data groups which are not acquired from the same time point, or have significant difference at the baseline are unable to be compared by the classic differential statistical methods, or the outcome will be without robust statistical support. In this paper, we described the characteristics of the Zheng studies of Chinese medicine. Then some data analytic methods based on machine learning are introduced as potential solutions for the data processing in the CER research of Chinese medicine. Finally, a new strategic framework is introduced to establish the CER methodology for Chinese medicine.Bioinformatics and Biomedicine Workshops (BIBMW), 2012 IEEE International Conference on; 01/2012
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ABSTRACT: As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.Evidence-based Complementary and Alternative Medicine 08/2015; 2015(5):376716. DOI:10.1155/2015/376716 · 1.88 Impact Factor
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