数据挖掘技术在中医诊疗数据分析中的应用

Application of Data Mining Technology for Data Analysis of TCM Diagnosis and Treatment

经过中华民族几千年的传承和发展,中医学形成了特有的理论体系,其疗效受到广泛认可,但因中医理论更关注有关疾病的症、治、效之间的关联关系,而不是现代自然科学所探究的因果关系,导致其科学性屡遭质疑。近年来,基于真实世界的临床研究模式和"大数据"理念日益受到重视,因此,越来越多的研究人员开始将研究重点放在疾病干预措施与结局指标的相关关系上,这一转变以及计算机数据挖掘与分析技术的进步,无疑给中医理论与实践的进一步发展带来重大契机。本文归纳总结了近年来中医诊疗中用到的数据挖掘技术,如聚类分析、决策树、贝叶斯网络、神经网络和多示例学习等,展示了如何运用这些方法从大量中医病症数据中揭示中医诊疗规律,发现隐藏在数据中的知识,以数据为支撑而显示中医学的有效性。

Through several thousand years’ inheritance and development by Chinese people, traditional Chinese medicine (TCM) has formed its unique theoretic system, whose efficacy has been widely accepted. However, because TCM theory focuses on the relationships among syndromes, treatment and efficacy, instead of the cause-and-effect relationship explored by modern natural science, the scientificity of TCM has always been questioned. In recent years, because virtual-world clinical research mode and the concept of “big data” were emphasized, increasing researchers began to put their research emphasis on the correlativity between intervening measures of diseases and outcome indicators. This change and the advancement of computer data mining and analysis technology, bring great opportunities for the further development of TCM theory and practice. This article concluded data mining technology used in TCM diagnosis and treatment in recent years, such as clustering analysis, decision tree, Bayesian network, neural network and multi-instance learning, which showed how to apply these methods to reveal rules of TCM diagnosis and treatment from a large number of TCM syndrome data, find knowledge hidden in data, and show TCM effectiveness supported by data.