Health informatics � Classification of traditional Chinese medicine data sets

2019 ◽  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Health Informatics ◽  
Data Sets

scholarly journals Classification of interventions in Traditional Chinese Medicine

2018 ◽  
Vol 38 (2) ◽  
pp. 315-320 ◽  
Author(s):  
Deng Hongyong ◽  
Clive E Adams ◽  
Farhad Shokraneh ◽  
Liang Shanghua
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine

scholarly journals Stability of Active Ingredients of Traditional Chinese Medicine (TCM)

2009 ◽  
Vol 4 (12) ◽  
pp. 1934578X0900401 ◽  
Author(s):  
Wang Meng ◽  
Ren Xiaoliang ◽  
Gao Xiumei ◽  
Franco Francesco Vincieri ◽  
Anna Rita Bilia
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Animal Origin ◽  
Active Ingredients ◽  
Rational Development ◽  
Plant Constituents ◽  
Crude Drugs ◽  
The Stability ◽  
First Time

Studies on stability of active ingredients are fundamental and critical for the rational development of Traditional Chinese Medicine (TCM) in view of its modernization and worldwide use. The stability of both active and marker constituents of plants used in TCM is reviewed for the first time. More than 100 papers, mostly written in Chinese, have been reviewed. Studies concerning plant constituents were analyzed according to their chemical classification of active ingredients. In addition, several crude drugs of animal origin are also reported. Stability of active ingredients is summarized during extraction and/or storage of the herbal drug preparations, and under stress conditions (pH, temperature, solvents, light, and humidity) and in the presence of preservatives, antioxidants, and metals.

A Computer Method for Validating Traditional Chinese Medicine Herbal Prescriptions

2005 ◽  
Vol 33 (02) ◽  
pp. 281-297 ◽  
Author(s):  
J. F. Wang ◽  
C. Z. Cai ◽  
C. Y. Kong ◽  
Z. W. Cao ◽  
Y. Z. Chen
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Statistical Learning ◽  
Conventional Medicine ◽  
Computer Method ◽  
Support Vector ◽  
Random Combination ◽  
System A ◽  
Statistical Learning Method

Traditional Chinese medicine (TCM) has been widely practiced and is considered as an alternative to conventional medicine. TCM herbal prescriptions contain a mixture of herbs that collectively exert therapeutic actions and modulating effects. Traditionally defined herbal properties, related to the pharmacodynamic, pharmacokinetic and toxicological, as well as physicochemical properties of their principal ingredients, have been used as the basis for formulating TCM multi-herb prescriptions. These properties are used in this work to develop a computer program for predicting whether a multi-herb recipe is a valid TCM prescription. This program is based on a statistical learning method, support vector machine (SVM), and it is trained by using 575 well-known TCM prescriptions and 1961 non-TCM recipes generated by random combination of TCM herbs. Testing results by using 72 well-known TCM prescriptions and 5039 non-TCM recipes showed that 73.6% of the TCM prescriptions and 99.9% of non-TCM recipes are correctly classified by this system. A further test by using 48 TCM prescriptions published in recent years found that 68.7% of these are correctly classified. These accuracies are comparable to those of SVM classification of other biological systems. Our study indicates the potential of SVM for facilitating the analysis of TCM prescriptions.

scholarly journals Detection of Herb-Symptom Associations from Traditional Chinese Medicine Clinical Data

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Bing Li ◽  
Xue-Zhong Zhou ◽  
Run-Shun Zhang ◽  
Ying-Hui Wang ◽  
Yonghong Peng ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Clinical Data ◽  
Multiple Testing ◽  
Large Scale ◽  
Individualized Medicine ◽  
Correlation Method ◽  
Data Sets ◽  
Chi Square ◽  
Positive Correlations

Background. Traditional Chinese medicine (TCM) is an individualized medicine by observing the symptoms and signs (symptoms in brief) of patients. We aim to extract the meaningful herb-symptom relationships from large scale TCM clinical data.Methods. To investigate the correlations between symptoms and herbs held for patients, we use four clinical data sets collected from TCM outpatient clinical settings and calculate the similarities between patient pairs in terms of the herb constituents of their prescriptions and their manifesting symptoms by cosine measure. To address the large-scale multiple testing problems for the detection of herb-symptom associations and the dependence between herbs involving similar efficacies, we propose a network-based correlation analysis (NetCorrA) method to detect the herb-symptom associations.Results. The results show that there are strong positive correlations between symptom similarity and herb similarity, which indicates that herb-symptom correspondence is a clinical principle adhered to by most TCM physicians. Furthermore, the NetCorrA method obtains meaningful herb-symptom associations and performs better than the chi-square correlation method by filtering the false positive associations.Conclusions. Symptoms play significant roles for the prescriptions of herb treatment. The herb-symptom correspondence principle indicates that clinical phenotypic targets (i.e., symptoms) of herbs exist and would be valuable for further investigations.

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