Yang Ming, Li Jia-qi, Jiao Li-jing, Chen Pei-qi, Xu Ling
Zhongguo Zhong Yao Za Zhi. 2015 Nov;40(22):4482-90.
The study on the effective core formulae (CEF) not only summarized traditional chinese medicine (TCM) treatment experience, but also helped reveal the underlying knowledge in the formulation of TCM prescriptions. The aim of the present paper was to investigate the method of data mining for the discovery of core effective formulae for lung cancer. In the present study, a prescription fingerprint approach was used to characterize the staged prescription information of patients. The D index was used to screen potential beneficial herbs. Then, based on a herbal compatibility network, the maximal clique searching algorithm (BK algorithm) and survival analysis were applied to discover CEF for lung cancer, and a mining analysis was made for the 322 cases from Longhua hospital. The correlation between prescriptions and survival time was analyzed by prescription fingerprints. Forty-three potentially beneficial herbs were obtained, and two CEFs were significant for the survival time by a parametric survival model based on lognormal distribution, the results were verified by a multivariate survival model. The rules of combination of the two CEFs basically conform to TCM onco-therapeutic theory of strengthening the body resistance and the actual conditions in clinic. All results showed that the established approach was feasible for discovering the core effective formulae for lung cancer and mining survival data for complex TCM onco-therapy.
对有效核心方剂（CEF）的研究不仅总结了中医治疗经验，还有助于揭示中医方剂配伍的潜在知识。本文旨在探讨数据挖掘方法以发现肺癌的核心有效方剂。在本研究中，采用方剂指纹图谱方法表征患者的分期方剂信息。用D指数筛选潜在的有益草药。然后，基于草药配伍网络，应用最大团搜索算法（BK算法）和生存分析来发现肺癌的CEF，并对龙华医院的322例病例进行挖掘分析。通过方剂指纹图谱分析方剂与生存时间之间的相关性。获得了43种潜在的有益草药，基于对数正态分布的参数生存模型显示有两种CEF对生存时间有显著影响，结果经多变量生存模型验证。两种CEF的配伍规律基本符合中医扶正抗癌的治疗理论及临床实际情况。所有结果表明，所建立的方法对于发现肺癌的核心有效方剂以及挖掘复杂中医肿瘤治疗的生存数据是可行的。
