With the rapid development of “Internet plus”, medical care has entered the era of big data. However, there is little research on medical big data (MBD) from the perspectives of bibliometrics and visualization. The substantive research on the basic aspects of MBD itself is also rare. This study aims to explore the current status of medical big data through visualization analysis on the journal papers related to MBD. We analyze a total of 988 references which were downloaded from the Science Citation Index Expanded and the Social Science Citation Index databases from Web of Science and the time span was defined as “all years”. The GraphPad Prism 5, VOSviewer and CiteSpace softwares are used for analysis. Many results concerning the annual trends, the top players in terms of journal and institute levels, the citations and H-index in terms of country level, the keywords distribution, the highly cited papers, the co-authorship status and the most influential journals and authors are presented in this paper. This study points out the development status and trends on MBD. It can help people in the medical profession to get comprehensive understanding on the state of the art of MBD. It also has reference values for the research and application of the MBD visualization methods.

Sustainability (SUS) is a journal in the field of environmental, cultural, economic and social sustainability of human beings and civilization, which was founded in 2009. This paper provides a comprehensive bibliometric overview of the journal and 6459 publications from 2009 to 2018. In the paper, we first introduce the materials and methods used. Next, we provide the bibliometric results in four parts. In the first part, we present the publication structure and citation structure of SUS, including annual trends of publications and citations, sources that cite SUS publications, and the most highly cited papers in SUS. The primary influential countries and institutes as well as their co-authorship networks are illustrated in the second part. The co-citation networks of cited references, journals and authors are shown in the third part. Finally, the co-occurrence network of keywords and bursting citation keywords is detected. VOSviewer and CiteSpace software packages are used for graphical visualization.

The current societal demands and technological developments have resulted in the participation of a large number of experts in making decisions as a group. Conflicts are imminent in groups and conflict management is complex and necessary especially in a large group. However, there are few studies that quantitatively research the conflict detection and resolution in the large-group context, especially in the multicriteria large-group decision making (GDM) context. This article proposes a dynamic adaptive subgroup-to-subgroup conflict model to solve multicriteria large-scale GDM problems. A compatibility index is proposed based on two kinds of conflicts among experts: 1) cognitive conflict and 2) interest conflict. Then, the fuzzy c-means clustering algorithm is used to classify experts into several subgroups. A subgroup-to-subgroup conflict detection method and a weight-determination approach are developed based on the clustering results. Afterward, a conflict resolution model, which can dynamically generate feedback suggestion, is introduced. Finally, an illustrative example is provided to demonstrate the effectiveness and applicability of the proposed model.

The widespread utilization of novel technological and societal paradigms gave birth to the development large-scale group decision making. This study focuses on two issues regarding the structure of the large group. The first is about the overlapping clusters/communities (interdependent subgroups) of large-scale experts identified from the dimension reduction process. The second is regarding the situation in which internal and external experts (i.e., heterogeneous experts) exist in a large-scale group decision-making problem simultaneously. To manage the first issue, we propose to use an existing overlapping community detection method to cluster experts considering the independencies of communities. To address the second issue, a delegation mechanism is introduced to allocate the trust weights of external experts to internal experts. Furthermore, to reach a consensus for large-scale experts, a consensus measure based on two reference points (global collective preference and community preference) is given. Finally, an illustrative example regarding 5G technology investment is provided to verify the applicability of the proposed model.