动态不确定因果图在中医诊断中的应用探讨

Discussion on Application of Dynamic Uncertain Causal Graph in Traditional Chinese Medicine Diagnosis

动态不确定因果图(DUCG)已成为中医药领域新兴的、先进的知识表示与推理模型。为更好地应用DUCG为中医临床提供诊断推理与决策支持,在归纳总结DUCG中医药领域研究与应用情况的基础上,分析现阶段DUCG在中医诊断中存在的主要问题,包括中医术语规范统一和中医药知识库质量问题、推理算法和模型建造的方法选择与设计问题、DUCG中医诊断模型的平台化和产品化问题等,并据此展开应用思路与方法探讨,提出应深挖DUCG的技术内涵,根据临床实际需求选择精准、高效的推理建模方法,建立符合中医药理论思想、具有中医特色的智能辅助诊断模型,加强DUCG协同研究平台及产品的开发应用。

Dynamic uncertainty causality graph(DUCG)has emerged as an advanced knowledge representation and reasoning model in the field of traditional Chinese medicine(TCM).In order to better use DUCG to provide diagnostic reasoning and decision-making support for TCM in clinic,in this paper the main problems of DUCG in TCM diagnosis at this stage were discussed,based on summarizing the research and application of DUCG in TCM field including the unification of TCM terminology standardization and the quality of TCM knowledge base,the selection and design of inference algorithms and model construction methods,and the platformization and productization of DUCG TCM diagnostic models.According to the problems,the application ideas and methods were discussed and it was put forwards that the technical connotation of DUCG should be deeply excavated,precise and efficient inference modeling methods should be selected according to the actual clinical needs,intelli-gent auxiliary diagnostic models consistent with the theoretical ideas of TCM and with the characteristics of TCM should be built,and the development and application of DUCG collaborative research platforms and products should be strengthened.