Analysis on the Medication Rules and Application Characteristics of Medicinal Herbs Asparagus Officinalis From Tibetan Medicine and Food
Objective:To explore the medication rules and application characteristics of prescription containing aspara-gus officinalis based on data mining.Methods:A database of Asparagus officinalis formula was established with Ti-betan medicine formula Daquan and Tibetan medicine classic literature integration as data sources,and the included formulas were analyzed by Excel 2021 office software,IBM SPSS Modeler 18.0,IBM SPSS Statistics 23.0,etc.Results:Eighty-nine prescriptions containing Asparagus officinalis were finally included,involving a total of 274 Tibetan medicines,which were used a total of 1 352 times cumulatively,and 25 high-frequency single herbs(≥15)that appeared at the same time were Polygonatum cirrhifolium(78),Tribulus terrestris(57),Himalayan purple jasmine(56),and so on.Analysis of the taste effect of high frequency single herbs(≥20)showed that sweet taste accounted for the largest proportion of herbs,the medicinal properties were mainly warm,and the efficacy was mainly stomach strengthening and kidney tonifying.Attending diseases were mainly kidney cold disease,gynecological disease,yellow water disease.Association rule analysis showed that the support degree of Asparagus-Polygonatum cirrhifoliu and Tribulus terrestris was higher in the drug pair combination,and Asparagus-Polygonatum cirrhifolium-Himalayan purple jasmine was the highest in the three herb groups.In the treatment of kidney cold disease,the five roots of Tibetan medicine were commonly occurring combinations,and cluster analysis found five basic drug combinations for the treatment of kidney cold disease.Conclusion:Asparagus officinalis mainly appears in the prescription of treating kidney cold disease,and its compatibility rules follow the basic theory of Tibetan medicine"three stom-ach fire"balance and the principle of syndrome differentiation and treatment.
Asparagus OfficinalisTibetan Medicine FormulaMedication RulesData Mining