The Research On Drug Laws Of Depression Based On The Modern Medical Recard

Purpose:Collect China periodical full-text database of ten years on the depression literature, datamining on the related literature,to summarize the prescription medication in the treatment of depression of the physicians.Method:The use of China CNKI, VIP database collecting the literature about2005-2015 years of depression, which are selected from the literatures met the inclusion criteria, the use of Microsoft Office Excel 2013 to establish the database, using frequency analysis method, cluster analysis method of high frequency drugs, medicine and is closely linked to the drug study, clustering.Result:A Collected a total of 312 cases, 260 drugs, medication frequency statistics show: the total frequency of 3969 times, ranked in the top 10 drugs were: bupleurum root, Poria, radix curcumae, Rhizoma Cyperi Rhizoma Pinelliae,Polygala, gardenia, oyster, Radix Angelicae sinensis. The medication frequency in more than 50 times were 26 kinds, accounting for the total of 53% medication frequency. In the commonly used drugs, involving a total of nineteen drugs,mainly for Qi regulating drugs, antipyretic, tonic, sedative,expectorant antiasthmatic, as the main drug in the treatment of depression. Secondly, for promoting blood circulation and removing blood stasis, relieving drugs,diuretics for eliminating dampness, astringent drug, Chinese medicine, dampness etc., also has an important role in the treatment of depression. According to the use frequency in more than 20 times that of 57 herbs, 5 groups and 10 of the commonly used drug for depression, embodies the gas, fire, wet, blood, phlegm and other pathological factors.Conclusion : Drug treatment of depression treatment with medicine for regulating qi and phlegm dampness medicine, Radix Bupleuri, pinellia, Poria,radix curcumae, Rhizoma Cyperi drug; statistics results showed that Qi and clearing heat drugs drugs drugs such as the core, through the frequency analysis and cluster analysis methods of data mining, can discover hidden regularity in depression in medical records for clinical syndrome differentiation and treatment, and provide theoretical and practical guidance.