摘要
目的:利用文本挖掘技术探索贫血中医证治方药规律。方法:在中国生物医学文献服务系统中收集治疗贫血的文献数据,采用基于敏感关键词频数统计的数据分层算法,挖掘贫血中医证候、治法、汤药、中药规律。这些规律通过一维频次表及二维网络图进行展示。结果:贫血证候以虚证为主,常见于气、血、阴、阳亏虚,病变涉及肝、脾、肾等脏腑。治法方面,以补法为多,各种补益方法常与活血化瘀法配合使用。汤药以补气、养血活血功效为主,以归脾汤、四物汤、当归补血汤为最常用。中药以补气养血活血药为主,当归、白术、人参、甘草、大枣、黄芪应用最多。结论:文本挖掘能够比较客观地总结中医证治方药规律,为临床应用提供有益的探索和参考。
Objective：To explore the regularityof syndrome and therapeutic principles on anemia with text mining technique. Method： The data was from Chinese BioMedical literature database. The regularities of traditional Chinese medicine （TCM） syndrome, TCM treatment method, TCM decoction and herb on anemia were mined out by data slicing algorithm. The results were demonstrated with tables and networks. Result： The deficiency syndrome was the main TCM pattern of anemia. It generally included the deficiency of liver, spleen and kidney. Reinforcing method was usually used and it was often combined with the method of activating blood and resolving stasis. The herbal decoction was focus on the functions of replenishing qi, blood and activating blood. The decoctions such as Guipitang, Siwutang and Dangguibuxuetang were commonly used. The main function of herbal pieces was the same as decoctions. The herbal pieces such as angelica,atractylodes, ginseng, glycyrrhiza, jujube and astragaluswere most often used. Conclusion： Text mining approach provides an important method in exploring the regularities of syndrome and therapeutic principles.
出处
《中国实验方剂学杂志》
CAS
北大核心
2013年第5期341-344,共4页
Chinese Journal of Experimental Traditional Medical Formulae
基金
国家自然科学基金杰出青年项目(30825047)
国家自然科学基金面上项目(30973975
81072982)
国家自然科学青年基金项目(30902003)
中国博士后基金面上项目(20110940553)
中国中医科学院自主选题项目(Z0172)
关键词
文本挖掘
数据分层算法
贫血
证治方药
text mining technique
data slicing algorithm
anemia
syndrome and therapeutic principles
