PDF(Pubmed)
Abstract：
When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic\'s main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID-19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters.
摘要：
2019年12月，当COVID-19在中国传播时，成千上万的研究都集中在这种大流行上。每个人都有一个独特的视角，反映了大流行的主要科学学科。例如,社会科学家关注的是减少对人类精神状态的心理影响，特别是在封锁期间。计算机科学家专注于建立快速准确的计算机化工具来帮助诊断，预防,从疾病中恢复过来。医学科学家和医生，或者前线，是收到的主要英雄，治疗,并以牺牲自己的健康为代价处理了数百万病例。他们中的一些人甚至以生命为代价继续工作。所有这些研究都强化了多学科的工作，来自不同学科的科学家(社会，环境,技术,等。)联合起来，在危机期间为有益的结果进行研究。计算机科学及其各种技术是众多分支之一，包括人工智能，物联网,大数据,决策支持系统(DSS)还有更多。最值得注意的DSS利用是与多准则决策(MCDM)相关的利用，它应用于各种应用程序和许多上下文，包括商业,社会,技术和医学。由于其在制定正确的决策方案和准确判断的预防策略方面的重要性，它被认为是广泛探索的一个值得注意的话题，特别是在与COVID-19相关的医疗应用中。本研究是使用系统评价方案对COVID-19相关MCDM医学案例研究的综合评价。PRISMA方法被用来从四个主要的科学数据库(ScienceDirect，IEEEXplore,Scopus,和WebofScience)。最终的文章集分为分类法，包括五组：（1）诊断（n=6），（2）安全（n=11），（3）医院（n=8），（4）治疗（n=4），和（5）审查（n=3）。还在年度科学生产的基础上进行了书目分析，国家科学生产，共现,和共同作者。还进行了全面的讨论，以讨论主要挑战，动机,以及在COVID-19相关医学病例研究中使用MCDM研究的建议。最后,我们通过相应的解决方案和详细的方法来确定关键的研究差距，以作为未来方向的指南。总之,MCDM可以有效地用于医学领域，以优化资源并做出最佳选择，尤其是在大流行和自然灾害期间。
