Application of genetic algorithm for discovery of core effective formulae in TCM clinical data.
Application of genetic algorithm for discovery of core effective formulae in TCM clinical data.
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DOI：
10.1155/2013/971272
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发表时间：
2013
影响因子：
--
通讯作者：
Xu L
中科院分区：
文献类型：
--
作者：
Yang M;Poon J;Wang S;Jiao L;Poon S;Cui L;Chen P;Sze DM;Xu L
核心有效方的研究不仅可以总结中医药治疗经验，而且有助于揭示中医处方的基本知识。本文讨论了从肿瘤临床数据中发现CEF。定义了CEF的信心、支持和有效性的概念。遗传算法（GA）被应用于从肺癌数据集，从161例患者的595个记录中找到CEF。结果表明，有9个CEF与15种不同的草药具有正的适应值。持续进修基金的平均信心和支持率均较高。结果表明，中药复方中的中药均为核心中药。将数据集分为CEF组和非CEF组。治疗组有效率明显高于对照组。用协同指数（SI）评价两种中药之间的相互作用。有4对具有高SI值的草药，以表明草药之间的协同作用。所有结果均符合中医理论，证明了该方法的可行性。
Research on core and effective formulae (CEF) does not only summarize traditional Chinese medicine (TCM) treatment experience, it also helps to reveal the underlying knowledge in the formulation of a TCM prescription. In this paper, CEF discovery from tumor clinical data is discussed. The concepts of confidence, support, and effectiveness of the CEF are defined. Genetic algorithm (GA) is applied to find the CEF from a lung cancer dataset with 595 records from 161 patients. The results had 9 CEF with positive fitness values with 15 distinct herbs. The CEF have all had relative high average confidence and support. A herb-herb network was constructed and it shows that all the herbs in CEF are core herbs. The dataset was divided into CEF group and non-CEF group. The effective proportions of former group are significantly greater than those of latter group. A Synergy index (SI) was defined to evaluate the interaction between two herbs. There were 4 pairs of herbs with high SI values to indicate the synergy between the herbs. All the results agreed with the TCM theory, which demonstrates the feasibility of our approach.