发表机构
Faculty of Engineering Science, College of Engineering, University of Tehran; Department of Engineering Science, College of Engineering, University of Tehran（德黑兰大学工程学院科学学部; 德黑兰大学工程学院工程科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出双极模糊关系优化框架，结合正、负模糊关系矩阵与模糊需求向量，开发整合多环节的算法，经CDSS示例验证可将模糊临床关系信息转化为优化的决策推荐。
AI 中文摘要
临床决策支持常涉及异质测量、专家评估和基于指南的知识，这些与临床决策的关系是渐进的，而非纯粹二元的。本文提出一种基于双极模糊关系优化的临床决策支持框架，其中有利和不利的临床关系通过正、负模糊关系矩阵与模糊需求向量共同表示。每个决策变量及其补集同时参与关系约束，使双极临床信息可被纳入统一优化模型。采用最小t-范数对模糊关系等级与决策水平间的交互提供非补偿性的瓶颈型解释。通过推导临床容许区间和有效证据激活集来刻画可行推荐集，为可行性分析和关系系统的系统性简化提供基础。随后将完整可行集表示为与容许临床证据分配函数相关的临床推荐区域的有限并集。为每个容许证据分配构造分区域最优候选，对所得有限候选集进行比较以得到全局最优推荐。开发了整合可行性分析、系统简化、分区域优化和全局选择的算法，并通过一个数值CDSS（临床决策支持系统）示例进行说明。结果表明，双极模糊关系优化为将模糊临床关系信息转化为可行且优化的决策支持推荐提供了结构化且数学严谨的机制。
英文摘要
Clinical decision support frequently involves heterogeneous measurements, expert assessments, and guideline-based knowledge whose relationships to clinical decisions are gradual rather than purely binary. This paper develops a clinical decision-support framework based on bipolar fuzzy relational optimization, in which favorable and unfavorable clinical relationships are represented jointly through positive and negative fuzzy relational matrices together with a fuzzy requirement vector. The simultaneous participation of each decision variable and its complement in the relational constraints enables bipolar clinical information to be incorporated within a unified optimization model. The minimum \(t\)-norm is adopted to provide a noncompensatory, bottleneck-type interpretation of the interaction between fuzzy relational grades and decision levels. We characterize the feasible recommendation set by deriving clinical admissibility intervals and effective evidence activation sets, which provide the basis for feasibility analysis and systematic reduction of the relational system. The complete feasible set is then represented as a finite union of clinical recommendation regions associated with admissible Clinical Evidence-Assignment Functions. A region-wise optimal candidate is constructed for each admissible evidence assignment, and comparison of the resulting finite collection of candidates yields a globally optimal recommendation. An algorithm integrating feasibility analysis, system reduction, region-wise optimization, and global selection is developed and illustrated through a numerical CDSS example. The results show that bipolar fuzzy relational optimization provides a structured and mathematically rigorous mechanism for transforming fuzzy clinical relational information into feasible and optimized decision-support recommendations.
