医疗保障任务风险评估:一项结合判断矩阵与群智能优化算法的研究

Risk assessment for medical support missions:a study combining judgement matrices with swarm intelligence optimization algorithm

目的提出一种快速智能优化算法,使在通过层次分析法(AHP)对医疗保障分队任务进行风险评估时存在的判断矩阵不满足一致性的问题得到解决,同时最大程度保留专家原始信息。方法在原判断矩阵的基础上,充分结合各指标间两两重要性程度,通过特征向量法构造一个各指标之间重要性程度完全合理的矩阵作为过渡矩阵。进而以一定比例系数将两个矩阵结合,使结合成的新矩阵既满足指标间重要程度比较的合理性,又能最大程度保留原矩阵的信息。为了找到最优的比例系数,结合群智能优化算法,设置优化目标为新矩阵与原始矩阵的相似度,约束条件为矩阵满足一致性检验。最终找到最优的比例系数,并将在此系数下形成的新矩阵作为优化后的矩阵。结果该算法可以优化AHP在医疗保障任务风险评估应用中判断矩阵的一致性,有效解决原始矩阵不满足一致性检验的问题,并最大程度保留专家的原始意图。结论本算法实现了智能优化判断矩阵的一致性,使AHP在医疗保障任务风险评估、医疗安全风险评估等复杂系统的应用中更具科学性与合理性。

Objective To propose a fast and intelligent optimization algorithm to address the issue of inconsistent judgment matrices when using the analytic hierarchy process(AHP)for risk assessment of medical support team tasks,while preserving the original expert information as much as possible.Methods On the foundation of the original judgment matrix,thorough consideration was given to the pairwise importance levels among indicators.By employing the eigenvector method,a new matrix was constructed where the degrees of importance among indicators were fully rational,serving as a transitional matrix.Subsequently,by integrating the two matrices at a specifically determined ratio,the resultant composite matrix aimed to both uphold the reasonableness of inter-indicator comparisons and preserve the integrity of the initial matrix's information to the greatest extent.In pursuit of the optimal ratio coefficient,swarm intelligence optimization algorithms were introduced,with the optimization objective set as the similarity between the new matrix and the original one,under the constraint that the matrix complied with consistency checks.Ultimately,the optimal ratio coefficient was identified,and the matrix formed under this coefficient was adopted as the optimized matrix.Results The algorithm was capable of enhancing the consistency of the judgment matrices employed in the application of AHP for medical support mission risk assessment.It effectively addressed the issue of original matrices failing to meet consistency checks,while also preserving,to the greatest extent,the original intent of the experts involved.Conclusion This algorithm realizes the intelligent optimization of consistency in judgment matrices,thereby enhancing the scientific and rational application of the AHP in complex systems such as risk assessment for medical support missions and medical safety.