基于CBR/RBR融合模式的医疗决策代价敏感性研究

项目名称： 基于CBR/RBR融合模式的医疗决策代价敏感性研究

项目编号： No.71201087

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 管理科学与工程

项目作者： 徐曼

作者单位： 南开大学

项目金额： 19万元

中文摘要： 基于规则/案例(CBR/RBR)融合推理模式应用于医疗决策的研究已取得初步理论与实践成果,但仍存在误判率、漏判率高的决策代价敏感性问题。本项目针对医疗决策中融合推理空间知识场特征,运用时间窗技术、传递熵原理构建基于同态性及其条件转移概率的转移代价状态方程,揭示融合推理机制中决策代价敏感性诱因、关键因素及作用。将临床和智能模拟病房结合,获取与分析不同类别病例特征集及其诊断结论等决策代价损失数据,在CBR/RBR框架下建立融合推理模型,使其既可提高不完整性数据集推理效率,又可减小代价矩阵一定时的推理损失；运用奇异值分解定理,研究决策代价敏感性判别准则及决策解空间的收敛性。在不确定性与非平衡性条件下,构建考虑稳态与随机态关联性的贝叶斯网络学习模型；分析决策代价敏感性对推理效率与品质的影响,针对不同条件提出降低误判率、漏判的措施。通过实证研究对模型进行验证,将理论与仿真结果应用于医疗决策分析中。

中文关键词： 基于案例的推理；基于规则的推理；医疗决策；代价敏感性；

英文摘要： Fusion mode of CBR and RBR(FMC/R) has been using in researches on medical decision-making (MD)，and great achievements has been obtained in theory and practice field. While there are also some problems on cost sensitivity in FMC/R, such as high false negative rate (FNR) and false positive rate (FPR). Aiming at characteristics of knowledge field in fusion reasoning space of MD, in this research, time window technique and transfer entropy are introduced to modeling state equation of transfer cost based on homomorphism and conditional transferred probability. Incentives, key factors and its influence of cost sensitivity are to be revealed in fusion reasoning mechanism. Combining both clinical and simulated data, the decision data of cost losses, such as feature sets of different categories from medical cases and their diagnostic results, is to be acquired and analyzed. FMC/R will be established to improve reasoning efficiency of incomplete data and to reduce reasoning losses with determined cost matrix. Based on singular value decomposition theorem, both sensitivity criterions of decision cost and convergence properties of solution spaces in MD will be explored. On circumstances of uncertainty and imbalance, Bayesian network learning model is to be built to explore the correlation between steady-state and random-sta

英文关键词： Case Based Reasoning；Rule Based Reasoning；Medical Decision Making；Cost-Sensitivity；