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Received Sep 15, 2017; Accepted Jan 16, 2018
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1. Introduction
Diarrhea predominate irritable bowel syndromes (IBS-D) is one of the most commonly seen diseases in traditional Chinese medicine (TCM) clinical practice. With complex clinical appearance and integrated affect, IBS-D has both psychological and physical effects on patients, which causes deterioration in their quality of life [1]. TCM approach has been reported as an effective alternative therapy in relieving related discomforts caused by IBS-D [2–4]. TCM pattern is defined as a summary of disharmony condition of body elements based on prestigious TCM theory such as Yin-Yang and Wu-Xing theory. Accurate pattern diagnosis of patients assists in a better treatment of disease [3, 5]. However, traditional approaches of pattern diagnosis subjectively rely on individual experience of the practitioner and there is no yet adequate evidence found for objective detection of TCM pattern.
The symptoms of IBS-D patients involve several systems, which is complex to make complete assessment for comprehensive evaluation of each patient. In recent years, methods of interdisciplinary research such as computer science and psychology have been imported and applied in disease detection and longitude management [6, 7]. Instruments with quantitative characteristics such as IBS-SSS [8] and IBS-QOL [9] have been developed for diseases evaluation or quality of life measurement for IBS. However, those scales are not suitable for TCM pattern detection due to differences between their theories. As far as TCM pattern is concerned, theoretical interactions between symptoms and pattern factor are complex with multidimensionality. Thus, instruments developed in classical test theory with linear unidimensional assumption do not meet the requirement for quantitative analysis of pattern. Overall, as to IBS-D, there is a lack of instruments specially developed for quantitative severity detection of TCM pattern.
Against high-dimensionality assumption of the real-world, factor analysis methods such as exploratory factor analysis (EFA) and principal component analysis are commonly used to construct and simplify the theoretical model by finding out latent factors from observed variables. However, there are limitations about these methods in linear assumption because most of the systems including disease are inherently nonlinear.
Latent variable model...