Author_Institution :
Second Affiliated Hosp., Guangzhou Univ. of Chinese Med., Guangzhou, China
Abstract :
Objective: To analyze the component law of Chinese medicines for gout by mutual information mining. Methods: Chinese medicine prescriptions for gout were collected and recorded in the database, and then the frequency analysis of herbs, correlation coefficient between herbs, core combinations of herbs were analyzed by using modified mutual information, complex system entropy cluster and unsupervised hierarchical clustering, respectively. Results: Based on the analysis of 127 Chinese medicine prescriptions for gout, 44 drugs with high-frequency occurrence in these prescriptions, the distribution of nature, flavor and channel entry, 38 high frequency drug combinations, 10 drug combinations with higher confidence coefficient, 16 frequently-used herb pairs and 14 core combinations were founded. Conclusion: Chinese medicines for gout were mainly consist of Chinese medicines used for treating gout were main mainly consist of heat-clearing medicinal, blood-activating medicinal, dampness-draining diuretic medicinal, dampness-resolving medicinal. The treatment principle were main mainly consist of clear heat and dry dampness, induce diuresis to drain dampness, induce diuresis to alleviate edema, activate blood and resolve stasis. The treatment principle for gout is to eliminate the pathogenic factors. Radix Clematidis, Rhizoma Dioscoreae Septemlobae, Cortex Phellodendri, Semen Coicis, Poria, Radix Achyranthis Bidentatae, Rhizoma Atractylodis, Rhizoma Alismatis, Radix Glycyrrhizae and Rhizoma Anemarrhenae are the main herbal drugs used in the treatment of gout.
Keywords :
biothermics; blood; data mining; drugs; medical computing; medical disorders; patient treatment; pattern clustering; unsupervised learning; Chinese medicine prescriptions; Cortex Phellodendri; Poria; Radix Achyranthis Bidentatae; Radix Clematidis; Radix Glycyrrhizae; Rhizoma Alismatis; Rhizoma Anemarrhenae; Rhizoma Atractylodis; Rhizoma Dioscoreae Septemlobae; Semen Coicis; blood stasis; blood-activating medicinal; channel entry; clear heat; complex system entropy cluster; component law; correlation coefficient; dampness-draining diuretic medicinal; dampness-resolving medicinal; database; diuresis; drain dampness; dry dampness; edema; flavor; frequency analysis; frequency drug combinations; gout treatment; heat-clearing medicinal; herb core combinations; herbal drugs; high-frequency occurrence; medication regularity; modified mutual information; mutual information mining; nature distribution; pathogenic factors; resolve stasis; traditional Chinese medicine; treatment principle; unsupervised hierarchical clustering; Blood; Correlation coefficient; Data mining; Databases; Drugs; Medical diagnostic imaging; Mutual information; Chinese medicine; gout; mutual information mining; new prescription discovery; unsupervised data mining methods;
