一个模糊粗的贝叶斯网络模型,用于解决复杂的医院服务质量评估
He Li1, Mohammad Yazdi1,2, Hong-Zhong Huang3
1School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen, 518107 People's Republic of China.
概括
这项研究介绍了一种新的贝叶斯方针网络,用于医院服务质量评估. 该方法有效地处理动态特征和不确定性,改善复杂的医疗环境中的决策.
科学领域:
- 医疗保健管理的管理
- 决策科学 决策科学 决策科学
- 人工智能的人工智能
背景情况:
- 医院是医疗保健的核心,服务质量是关键因素.
- 医疗保健中的决策受到相互依赖,动态特征和不确定性的挑战.
- 现有的方法难以解决复杂系统中的客观和主观不确定性.
研究的目的:
- 开发一种先进的决策方法来评估医院服务质量.
- 为了整合贝叶斯网络,和模糊粗略集进行强大的分析.
- 解决质量评估中的动态特征以及客观和主观的不确定性.
主要方法:
- 利用贝叶斯偶网络来建模相互关系和联合概率分布.
- 采用模糊的粗略集合理论与邻里运营商来管理主观决策者证据.
- 开发了一个结合Copula贝叶斯网络和扩展模糊粗略设置技术的新框架.
主要成果:
- 提出的方法有效地减少了决策过程中的不确定性.
- 证明有能力评估医院服务质量的各种因素之间的依赖关系.
- 通过对伊朗医院服务质量的现实分析来验证.
结论:
- 综合方法为医疗保健中复杂的决策提供了一个强大的工具.
- 该方法成功地处理了动态特征和主观/客观不确定性.
- 为基于质量评估中的多重标准对替代品进行排名提供了一个新的框架.
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