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数据挖掘技术是分析复杂医疗病例的重要手段,但如何选择合适的指标将中医临床经验科学化以提高医生的诊断和治疗水平、并对其进行有效监控是中医急需解决的问题.针对这个问题,提出一种医疗指标约简的方法——基于聚类的贝叶斯网络模型,通过分类及进行主特征和类特征的提取来研究中医症候分型、动态演变,为类风湿关节炎病因、病机的研究及临床医生诊断质量控制提供了重要依据.
Data mining technology is an important means of analyzing complex medical cases, but how to choose suitable indicators to scientifically improve TCM clinical experience to improve doctor’s diagnosis and treatment level and monitor it effectively is an urgent problem to be solved by Chinese medicine.In response to this problem, A medical index reduction method is proposed based on clustering Bayesian network model. The classification of TCM features and the extraction of the main features and features are used to study TCM symptom type, dynamic evolution, the cause of rheumatoid arthritis, Machine research and clinician diagnosis quality control provides an important basis.