FOLLOWUS
Statistical identification of syndromes feature and structure of disease of western medicine based on general latent structure model
- Chinese Journal of Integrative Medicine Vol. 18, Issue 11, Pages: 850-861(2012)
- Affiliations：
1. School of Statistics, Renmin University of China,Beijing,China
2. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences,Beijing,China
- Author bio：
- Funds：Supported by Items of Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences Natural Science Fundation (No. 30873339)
DOI：10.1007/s11655-012-1118-7
CLC：Published：2012，
Published Online：19 July 2012，
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Yang, W., Yi, Dh., Xie, Ym. et al. Statistical identification of syndromes feature and structure of disease of western medicine based on general latent structure model., Chin. J. Integr. Med. 18, 850–861 (2012). https://doi.org/10.1007/s11655-012-1118-7
Wei Yang, Dan-hui Yi, Yan-ming Xie, et al. Statistical identification of syndromes feature and structure of disease of western medicine based on general latent structure model. [J]. Chinese Journal of Integrative Medicine 18(11):850-861(2012)
Yang, W., Yi, Dh., Xie, Ym. et al. Statistical identification of syndromes feature and structure of disease of western medicine based on general latent structure model., Chin. J. Integr. Med. 18, 850–861 (2012). https://doi.org/10.1007/s11655-012-1118-7 DOI：
Wei Yang, Dan-hui Yi, Yan-ming Xie, et al. Statistical identification of syndromes feature and structure of disease of western medicine based on general latent structure model. [J]. Chinese Journal of Integrative Medicine 18(11):850-861(2012) DOI： 10.1007/s11655-012-1118-7.
摘要
Syndrome differentiation is the character of Chinese medicine (CM). Disease differentiation is the principle of Western medicine (WM). Identifying basic syndromes feature and structure of disease of WM is an important avenue for prevention and treatment of integrated Chinese and Western medicine. The idea here is first to divide all patients suffering from a disease of WM into several groups in the light of the stage of the disease
and secondly to identify basic syndromes feature in a distinct stage
and finally to achieve the purpose of syndrome differentiation. Syndrome differentiation is simply taken as a classifier that classifies patients into distinct classes primarily based on overall observation of their symptoms. Previous clustering methods are unable to cope with the complexity of CM. We therefore show a new multi-dimensional clustering method in the form of general latent structure (GLS) model
which is a suitable statistical learning technique of latent class analysis. In this paper
we learn an optimal GLS model which reflects much better model quality compared with other latent class models from the osteoporosis patient of community women (OPCW) real data including 40-65 year-old women whose bone mineral density (BMD) is less than mean-2.0 standard deviation (M-2.0SD). Further
we illustrate a case analysis of statistical identification of CM syndromes feature and structure of OPCW from qualitative and quantitative contents through the GLS model. Our analysis has discovered natural clusters and structures that correspond well to CM basic syndrome and factors of osteoporosis patients (OP). The GLS model suggests the possibility of establishing objective and quantitative diagnosis standards for syndrome differentiation on OPCW. Hence
for the future it can provide a reference for the similar study from the perspective of a combination of disease differentiation and syndrome differentiation.
Abstract
Syndrome differentiation is the character of Chinese medicine (CM). Disease differentiation is the principle of Western medicine (WM). Identifying basic syndromes feature and structure of disease of WM is an important avenue for prevention and treatment of integrated Chinese and Western medicine. The idea here is first to divide all patients suffering from a disease of WM into several groups in the light of the stage of the disease
and secondly to identify basic syndromes feature in a distinct stage
and finally to achieve the purpose of syndrome differentiation. Syndrome differentiation is simply taken as a classifier that classifies patients into distinct classes primarily based on overall observation of their symptoms. Previous clustering methods are unable to cope with the complexity of CM. We therefore show a new multi-dimensional clustering method in the form of general latent structure (GLS) model
which is a suitable statistical learning technique of latent class analysis. In this paper
we learn an optimal GLS model which reflects much better model quality compared with other latent class models from the osteoporosis patient of community women (OPCW) real data including 40-65 year-old women whose bone mineral density (BMD) is less than mean-2.0 standard deviation (M-2.0SD). Further
we illustrate a case analysis of statistical identification of CM syndromes feature and structure of OPCW from qualitative and quantitative contents through the GLS model. Our analysis has discovered natural clusters and structures that correspond well to CM basic syndrome and factors of osteoporosis patients (OP). The GLS model suggests the possibility of establishing objective and quantitative diagnosis standards for syndrome differentiation on OPCW. Hence
for the future it can provide a reference for the similar study from the perspective of a combination of disease differentiation and syndrome differentiation.
关键词
latent structure analysisgeneral latent structure modelmulti-dimensional clusterBayesian
Keywords
latent structure analysisgeneral latent structure modelmulti-dimensional clusterBayesian
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