The application of TCM syndrome differentiation in internal diseases

2020 ◽  
Vol 2 (1) ◽  
pp. 12-16
Author(s):  
Jiang Hua
Keyword(s):  
Syndrome Differentiation ◽  
Tcm Syndrome

scholarly journals Treatment of Oral Ulcer Based on TCM Syndrome Differentiation

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Ying Yao ◽  
Li Liu
Keyword(s):  
Quality Of Life ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Western Medicine ◽  
Severe Case ◽  
Oral Ulcer ◽  
Syndrome Differentiation ◽  
Tcm Syndrome ◽  
Chinese Medicine Treatment

Oral ulcer is a kind of ulcerative injury that occurs in the oral mucosa and is very common in clinic. In severe case, it can affect the quality of life of the patients. Western medicine treatment of oral ulcer is often prone to relapse, while the effect of traditional Chinese medicine treatment is remarkable.

scholarly journals A Microcosmic Syndrome Differentiation Model for Metabolic Syndrome with Multilabel Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shujie Xia ◽  
Jia Zhang ◽  
Guodong Du ◽  
Shaozi Li ◽  
Chi Teng Vong ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Syndrome Differentiation ◽  
Diagnostic Model ◽  
Multisystem Disease ◽  
K Value ◽  
Multilabel Learning ◽  
Complex Correlation ◽  
Tcm Syndrome ◽  
The Relationship ◽  
Differentiation Model

Background. Metabolic syndrome (MS) is a complex multisystem disease. Traditional Chinese medicine (TCM) is effective in preventing and treating MS. Syndrome differentiation is the basis of TCM treatment, which is composed of location and/or nature syndrome elements. At present, there are still some problems for objective and comprehensive syndrome differentiation in MS. This study mainly proposes a solution to two problems. Firstly, TCM syndromes are concurrent, that is, multiple TCM syndromes may develop in the same patient. Secondly, there is a lack of holistic exploration of the relationship between microscopic indexes, and TCM syndromes. In regard to these two problems, multilabel learning (MLL) method in machine learning can be used to solve them, and a microcosmic syndrome differentiation model can also be built innovatively, which can provide a foundation for the establishment of the next model of multidimensional syndrome differentiation in MS. Methods. The standardization scale of TCM four diagnostic information for MS was designed, which was used to obtain the results of TCM diagnosis. The model of microcosmic syndrome differentiation was constructed based on 39 physicochemical indexes by MLL techniques, called ML-kNN. Firstly, the multilabel learning method was compared with three commonly used single learning algorithms. Then, the results from ML-kNN were compared between physicochemical indexes and TCM information. Finally, the influence of the parameter k on the diagnostic model was investigated and the best k value was chosen for TCM diagnosis. Results. A total of 698 cases were collected for the modeling of the microcosmic diagnosis of MS. The comprehensive performance of the ML-kNN model worked obviously better than the others, where the average precision of diagnosis was 71.4%. The results from ML-kNN based on physicochemical indexes were similar to the results based on TCM information. On the other hand, the k value had less influence on the prediction results from ML-kNN. Conclusions. In the present study, the microcosmic syndrome differentiation model of MS with MLL techniques was good at predicting syndrome elements and could be used to solve the diagnosis problems of multiple labels. Besides, it was suggested that there was a complex correlation between TCM syndrome elements and physicochemical indexes, which worth future investigation to promote the development of objective differentiation of MS.

Clinical Research on TCM Syndrome Differentiation of 218 Patients with Postoperative Colorectal Cancer

2011 ◽  
Vol 13 (6) ◽  
pp. 938-943 ◽  
Author(s):  
Xu Yun ◽  
Zhao Aiguang ◽  
Gu Xiaohong ◽  
Liu Jianping ◽  
Wu Yu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Clinical Research ◽  
Syndrome Differentiation ◽  
Tcm Syndrome
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