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一种新型行为三向决策模型及其在治疗新冠肺炎轻症中的应用。

A novel behavioral three-way decision model with application to the treatment of mild symptoms of COVID-19.

作者信息

He Shi-Fan, Wang Ying-Ming, Pan Xiaohong, Chin Kwai-Sang

机构信息

Decision Sciences Institute, Fuzhou University, Fujian, 350108, China.

Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong.

出版信息

Appl Soft Comput. 2022 Jul;124:109055. doi: 10.1016/j.asoc.2022.109055. Epub 2022 May 25.

Abstract

The Coronavirus Disease 2019 (COVID-19) has popularized since late December 2019. In present, it is still highly transmissible and has severe impact on the public health and global economy. Due to the lack of specific drug and the appearance of different variants, the selection of the antiviral therapy to treat the patients with mild symptom is of vital importance. Hence, in this paper, we propose a novel behavioral Three-Way Decision (3WD) model and apply it to the medicine selection decision. First, a new relative utility function is constructed by considering the risk-aversion behavior and regret-aversion behavior of human beings. Second, based on the relative utility function, some new rules are defined to calculate the thresholds and conditional probabilities in 3WD and some corresponding theorems are explored and proved. Next, a new information fusion mechanism in the framework of evidential reasoning algorithm is developed. Then, the decision results are obtained based on the Bayesian decision procedure and the principle of maximum utility. Finally, an example with large-scale data set and an example about medicine selection for COVID-19 are provided to show the implementation process and effectiveness of the proposed method. Comparative analysis and sensitivity analysis are also performed to illustrate the superiority and the robustness of the current proposal.

摘要

2019年冠状病毒病(COVID-19)自2019年12月下旬以来开始流行。目前,它仍具有高度传染性,对公众健康和全球经济造成严重影响。由于缺乏特效药物以及不同变体的出现,选择抗病毒疗法治疗轻症患者至关重要。因此,在本文中,我们提出了一种新颖的行为三支决策(3WD)模型,并将其应用于药物选择决策。首先,通过考虑人类的风险规避行为和后悔规避行为构建了一个新的相对效用函数。其次,基于相对效用函数,定义了一些新规则来计算三支决策中的阈值和条件概率,并探索和证明了一些相应的定理。接下来,在证据推理算法框架下开发了一种新的信息融合机制。然后,基于贝叶斯决策过程和最大效用原则获得决策结果。最后,提供了一个具有大规模数据集的示例以及一个关于COVID-19药物选择的示例,以展示所提方法的实现过程和有效性。还进行了比较分析和敏感性分析,以说明当前提议的优越性和稳健性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2033/9132434/a33729819259/gr1_lrg.jpg

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