Research on the Application of Dynamical Uncertainty Causality Graph in Intelligent Assisted Syndrome Differentiation of Traditional Chinese Medicine
Taking the syndrome differentiation of depression as an example,this paper carries out research on the application of DUCG in intelligent assisted syndrome differentiation of TCM,and improves the visualization degree of syndrome differentiation knowledge in the syndrome differentiation model and the interpretability of syndrome differentiation reasoning process in the intelligent assisted syndrome differentiation model of TCM.It organizes and analyzes authoritative documents on depression,acquires knowledge on syndrome differentiation of depression,and collects,screens and preprocesses medical records of depression.A DUCG knowledge base for depression intelligent assisted syndrome differentiation is constructed,in which the symptom knowledge,syndrome type knowledge and the relationship between them are expressed,and it combines DUCG inference machine to conduct testing and analysis of syndrome differentiation reasoning.It constructs a DUCG knowledge base for depression intelligent assisted syndrome differentiation containing 19 subgraphs and a DUCG core knowledge base for depression intelligent assisted syndrome differentiation containing 6 subgraphs.The preliminary accuracy obtained through testing of syndrome differentiation reasoning can reach 72.92%,and the highest accuracy achieved by grouping statistics according to syndrome types can reach 100%.The syndrome differentiation results can be explained in detail according to the DUCG diagram.Applying DUCG theory to the research of intelligent assisted syndrome differentiation of TCM can help improve the visualization of syndrome differentiation knowledge and the interpretability of syndrome differentiation reasoning process in syndrome differentiation models.
国家科技期刊平台
NSTL
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