English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

Text Mining on Chinese Herbal Medicine Rule Exploration for Ovarian Cyst