Explore the medication rules of Chinese medicine of Professor Huang Wenzheng for the treatment of chronic kidney disease based on data mining

Journal Title: Traditional Medicine Research - Year 2016, Vol 1, Issue 3

Abstract

The study found the backbone network and three-level network of the Chinese medicine for the treatment of CKD by homogeneous network analysis. The 5 top commonly used herbs were Dan Shen (Radix Salviae Miltiorrhizae) , Fu Ling (Poria) , Huang Qin (Radix Scutellariae), Sheng Di Huang (Radix Rehmanniae Recens) and Sha Ren (Fructus Amomi Villosi), respectively. In recent years, chronic kidney disease (CKD) received increased attention due to its high prevalence and adverse effects. Current treatment options for CKD are limited. Many patients seek alternative therapies, such as traditional Chinese medicine (TCM). TCM has been used widely in the treatment CKD for many years. The purpose of this study is to explore the medication rules of Professor Huang Wenzheng's treatment of CKD with Chinese medicine based on data mining. The structured case database was built for 131 patients with CKD treated by Prof. Huang. The complex network analysis was applied to analyze the medication principle of Chinese medicines.The study found the backbone network and three-level network of the Chinese medicines for the treatment of CKD by homogeneous network analysis. The 5 top commonly used herbs were Dan Shen (Radix Salviae Miltiorrhizae) , Fu Ling (Poria) , Huang Qin (Radix Scutellariae), Sheng Di Huang (Radix Rehmanniae Recens) and Sha Ren (Fructus Amomi Villosi), respectively.

Authors and Affiliations

Dong Shao-Ning, Wang Yao-Guang, Xu Bing, Jin Liang, Wang Cui-Han

Keywords

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  • EP ID EP45291
  • DOI 10.12032/TMR201603021
  • Views 334
  • Downloads 0

How To Cite

Dong Shao-Ning, Wang Yao-Guang, Xu Bing, Jin Liang, Wang Cui-Han (2016). Explore the medication rules of Chinese medicine of Professor Huang Wenzheng for the treatment of chronic kidney disease based on data mining. Traditional Medicine Research, 1(3), -. https://www.europub.co.uk/articles/-A-45291