Research on Professor Zhang Huailiang's medication patterns for treating dizziness based on data mining
Objective:To explore the medication patterns of Professor Zhang Huailiang in the treatment of dizziness based on data mining technology.Method:Collect outpatient Chinese medicine prescription data of dizziness patients treated by Professor Zhang Huailiang from June 2019to August 2023,and use Excel to establish a database of the selected effective prescriptions.Use SPSS software,traditional Chinese medicine inheritance calculation platform,etc.to conduct multidimensional data mining on tra-ditional Chinese medicine prescriptions.Result:A total of 326prescriptions were included,involving 170traditional Chinese medi-cines.After analysis,it was found that the top 10high-frequency drugs are mainly Qingjin Zhimu,Rougan Yangxue;The high-frequency flavors are warm,flat,sweet,and bitter respectively;The spleen and liver have a higher frequency of meridian return;The efficacy mainly focuses on tonifying deficiency and clearing heat;Drug associations include"White Peony Scutellaria ba-icalensis"and"Poria cocos Scutellaria baicalensis";Cluster analysis has evolved into four drug groups.Conclusion:Professor Zhang Huailiang believes that if the treatment of dizziness is based on the differentiation of qi,it is advisable to focus on the Qi mechanism of the six organs and three jiao,with phlegm dampness,phase fire,and qi stagnation as the main pathological factors;In terms of blood classification,it is advisable to treat it from the three organs of liver,spleen,and kidney.Factors such as phlegm dampness,phase fire,qi stagnation,and blood stasis are combined with the deficiency of the root.In terms of treatment,dispelling evil without forgetting to support the right,there is an increase in the middle of the descending,and the combination of tonifying and purging can provide a certain reference for the clinical treatment of dizziness.
dizzinessData miningMedication patternsProfessor Zhang HuailiangPneumatic lifting and loweringLiu Fu San Jiao
国家科技期刊平台
NSTL
万方数据
