Text Mining on Chinese Herbal Medicine Rule Exploration for Ovarian Cyst

被引:0
|
作者
He, Dan [1 ]
Lu, Aiping [2 ]
Jiang, Miao [2 ]
Zheng, Guang [3 ]
Zhao, Ning [2 ]
Wang, Minzhi [2 ]
机构
[1] Shanghai Univ Tradit Chinese Med, E Res Inst, Shanghai, Peoples R China
[2] China Acad Chinese Med Sci, Inst Basic Res Clin Med, Beijing, Peoples R China
[3] Lanzhou Univ, Sch Informat Sci & Engn Technol, Lanzhou, Gansu, Peoples R China
关键词
text mining; ovarian cyst; medicine regularity;
D O I
暂无
中图分类号
C [社会科学总论];
学科分类号
03 ; 0303 ;
摘要
Ovarian cyst (OC) is one of the biggest concerns of women around the world. With the increase in the number of cases of OC, it seems like no woman is safe from this dreaded disease. Traditional Chinese Medicine (TCM) has its advantage in OC management, while due to the complexity and opacity; it is hard to clarify the rules of Chinese herbs. Text mining is a useful method to explore the regularity; we put this technology in principle research of Chinese herbal medicine (CHM) and associated it with patterns of TCM in OC treatment. The results we obtained from this study: Fuling, Guizhi, Taoren, Danpi, Chishao are top five herbs frequently used in OC. The pattern of Qi stagnation and blood stasis is the No. 1 syndrome, which is highly coincided with the top lists of the herbs. Conclusion: Text mining is a practical technology, which can help with the research field of medicine regularity and assist the physician with clinical decision; the future research shall be benefited from the outcome mined out by this technology.
引用
收藏
页码:224 / 226
页数:3
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