{"title":"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","authors":"Keyu Lu, Huchang Liao","doi":"10.1007/s10489-021-02909-y","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"52 12","pages":"13689 - 13713"},"PeriodicalIF":3.4000,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-021-02909-y.pdf","citationCount":"10","resultStr":"{\"title\":\"A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions\",\"authors\":\"Keyu Lu, Huchang Liao\",\"doi\":\"10.1007/s10489-021-02909-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":8041,\"journal\":{\"name\":\"Applied Intelligence\",\"volume\":\"52 12\",\"pages\":\"13689 - 13713\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2022-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s10489-021-02909-y.pdf\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10489-021-02909-y\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Intelligence","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10489-021-02909-y","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions
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.
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.