A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Intelligence Pub Date : 2022-01-05 DOI:10.1007/s10489-021-02909-y

Keyu Lu, Huchang Liao

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引用次数: 10

Abstract

Healthcare Industry 4.0 refers to intelligent operation processes in the medical industry. With the development of information technology, large-scale group decision making (GDM), which allows a larger number of decision makers (DMs) from different places or sectors to participate in decision making, has been rapidly developed and applied in Healthcare Industry 4.0 to help to make decisions efficiently and smartly. To make full use of GDM methods to promote the developments of the medical industry, it is necessary to review the existing relevant achievements. Therefore, this paper conducts an overview to generate a comprehensive understanding of GDM in Healthcare Industry 4.0 and to identify future development directions. Bibliometric analyses are conducted in order to learn the development trends from published papers. The implementations of GDM methods in Healthcare Industry 4.0 are reviewed in accordance with the paradigm of the general GDM process, which includes information representation, dimension reduction, consensus reaching, and result elicitation. We also provide current research challenges and future directions regarding medical GDM. It is hoped that our study will be helpful for researchers in the field of GDM in Healthcare Industry 4.0.

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医疗保健工业4.0中群体决策方法的调查：文献计量学、应用和方向

医疗行业4.0是指医疗行业的智能化操作流程。随着信息技术的发展，大规模群体决策（GDM）得到了快速发展，并在医疗保健工业4.0中得到了应用，它允许来自不同地方或部门的大量决策者参与决策，以帮助高效、智能地做出决策。为了充分利用GDM方法促进医疗行业的发展，有必要回顾现有的相关成果。因此，本文进行了概述，以全面了解医疗保健工业4.0中的GDM，并确定未来的发展方向。文献计量分析是为了从已发表的论文中了解发展趋势。根据通用GDM过程的范式，回顾了医疗保健工业4.0中GDM方法的实施，包括信息表示、降维、达成共识和结果引出。我们还提供了有关医学GDM的当前研究挑战和未来方向。希望我们的研究对医疗保健工业4.0中GDM领域的研究人员有所帮助。

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来源期刊

Applied Intelligence 工程技术-计算机：人工智能

CiteScore

6.60

自引率

20.80%

发文量

1361

审稿时长

5.9 months

期刊介绍： With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.