[Diagnosis and treatment rule of traditional Chinese medicine for syndrome factors of chronic congestive heart failure: a study based on Shannon entropy method]

Abstract

Objective: The primary aim of this research is to systematically sort out and analyze available clinical documents for chronic congestive heart failure in traditional Chinese medicine (TCM) and integrated traditional Chinese and Western medicine, and to explore the diagnosis and treatment rule of TCM syndrome factors with data mining method.

Methods: Based on the China National Knowledge Internet (CNKI) and Chinese Biomedical Literature Database (CBM), using "chronic heart failure" or "chronic congestive heart failure" or "chronic cardiac insufficiency" and "traditional Chinese medicine" or "syndrome" or "integrated traditional Chinese and Western medicine" as keywords for literature selection, we used Shannon entropy nonlinear complex system method for the feature extraction after data preprocessing.

Results: The statistics showed that yang deficiency, qi deficiency, blood stasis, water retention, yin deficiency, and turbid phlegm were principal TCM syndrome factors; herbs of qi-tonifying, blood-activating, water-draining, phlegm-dispelling, exterior-releasing, interior-warming were main drugs for clinical application. The symptoms and Chinese herbs which intimately connect with TCM syndrome factors were extracted, while their diagnostic contribution degrees were quantified by correlation coefficient. The symptoms which had high scores were general syndromes while the symptoms which had low scores were specific manifestations of a certain patient. Common Chinese herbs corresponding to each syndrome factor were picked out, indirectly reflecting their frequencies in clinical application.

Conclusion: Based on data mining of the relationship among the four diagnostic methods of TCM, syndrome factors and herbs, this study provided references for differentiation of TCM syndrome and selection of Chinese medicine according to clinical syndrome factors.