Text Mining on Chinese Herbal Medicine Rule Exploration for Ovarian Cyst
Dan He, Aiping Lu, Miao Jiang, Guang Zheng, Ning Zhao, Minzhi Wang
Available Online August 2013.
- https://doi.org/10.2991/icassr.2013.58How to use a DOI?
- text mining; ovarian cyst; medicine regularity
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Dan He AU - Aiping Lu AU - Miao Jiang AU - Guang Zheng AU - Ning Zhao AU - Minzhi Wang PY - 2013/08 DA - 2013/08 TI - Text Mining on Chinese Herbal Medicine Rule Exploration for Ovarian Cyst BT - 2013 International Conference on Applied Social Science Research (ICASSR-2013) PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/icassr.2013.58 DO - https://doi.org/10.2991/icassr.2013.58 ID - He2013/08 ER -