Progress of Traditional Chinese and Modern Medicine in Coronary Heart Disease

Coronary heart disease (CHD) is the leading cause of death. As the main treatment of CHD, modern medicine has improved dramatically in recent years. Although researches of TCM and integrative medicine on CHD are witnessed encouraging progress in many respects, the role TCM playing in the prevention and treatment of CHD has been unprecedentedly challenged under such circumstance of the very fast development of modern medicine. In order to share mutual complementary advantages of TCM and western medicine, this review summarizes the relatively prominent researches of TCM and integrative medicine on CHD in recent years, and illuminates the issue of the orientation of the further research of integrative medicine on CHD, including (1) original innovation of TCM etiology and pathogenesis, (2) combination of disease and TCM syndrome, (3) biological basis of TCM syndrome of CHD, (4) clinical design and quality control of integrative medicine research, (5) herb-drug interaction, (6) difficulties and hot issues of modern medicine.

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Blood stasis syndrome of coronary heart disease: A perspective of modern medicine

Chinese Journal of Integrative Medicine ◽

10.1007/s11655-013-1332-3 ◽

2013 ◽

Vol 20 (4) ◽

pp. 300-306 ◽

Cited By ~ 12

Author(s):

Gui Yu ◽

Jie Wang

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

Blood Stasis ◽

Modern Medicine ◽

Blood Stasis Syndrome

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Recognition Patterns Construction of Coronary Heart Disease Patients with Qi Stagnation Syndrome Based on Decision Tree

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.475-476.1025 ◽

2013 ◽

Vol 475-476 ◽

pp. 1025-1031

Author(s):

Qi Shi ◽

You Lin Li ◽

Hui Hui Zhao ◽

Jian Xin Chen ◽

Xin Qiu Wang ◽

...

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

Decision Tree ◽

Sensitivity And Specificity ◽

Biological Indicators ◽

Modern Medicine ◽

Biological Parameters ◽

Recognition Pattern ◽

High Incidence ◽

Qi Stagnation

Coronary heart disease (CHD), called Thoracic Obstruction in TCM, is one of the most important types of heart disease for its high incidence and high mortality. The methods of syndrome studies in TCM can not be completely in accordance with that of modern medicine because of the complexity itself. In this paper, we decide to investigate the ability of Decision Tree to predict CHD patients with or without qi stagnation syndrome. Predictions with CHAID Decision Tree (one type of the Decision Trees), we obtained recognition patterns made up of seven biological parameters. The accuracy of this diagnosis pattern was 80.7%, the sensitivity and specificity could reach 72.1% and 83.0%. The ADTree recognition pattern include six biological indicators. The accuracy of this diagnosis pattern was 94.6%, the sensitivity and specificity could reach 100% and 94.3%.

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Recognition Patterns Construction of Coronary Heart Disease Patients with Qi Deficiency Syndrome Based on Artificial Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.393-395.916 ◽

2011 ◽

Vol 393-395 ◽

pp. 916-920 ◽

Cited By ~ 1

Author(s):

Qi Shi ◽

Hui Hui Zhao ◽

Jian Xin Chen ◽

Yi Yang ◽

Cheng Long Zheng ◽

...

Keyword(s):

Neural Network ◽

Coronary Heart Disease ◽

Heart Disease ◽

Deficiency Syndrome ◽

Modern Medicine ◽

Biological Parameters ◽

Recognition Pattern ◽

Incidence And Mortality ◽

High Incidence ◽

Artificial Neural

Coronary heart disease (CHD), called “thoracic obstruction” in TCM, is one of the most important types of heart disease for its high incidence and mortality. The methods of syndrome studies in TCM can not be completely in accordance with these of modern medicine because of the complexity itself. In this paper, we investigated the ability of Artificial Neural Networks (ANNs) to predict CHD patients with or without qi deficiency syndrome. Predictions with Multilayer Perceptron Neural Network (MPLNN, one type of the ANNS), we obtained recognition patterns made up of eight biological parameters. The accuracy of this recognition pattern was 82.2%, and the accuracy of validation pattern was 80.0%.

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