Topic Clusters of Scientific Organizations of Moscow Healthcare Department: Distribution and Leaders

Introduction. Studying the evolution and life cycle of topics that form a particular field of knowledge is important in order to predict certain trends in the development of scientific research in any scientific field (including medicine), as these topics are crucial in identifying and developing new directions. The SciVal topic cluster system is one of the effective, appealing, and promising models for topic-based article classification. It is utilized on the SciVal analytical platform using data from the Scopus international scientific citation database. Materials and methods. Data collection and uploading were carried out as of January 24, 2023. The time frame of the study was four years, from 2019 to 2022. The goal was to examine a set of medical publications for 15 organizations (4 research institutes and 11 scientific and practical centers) subordinate to Moscow Healthcare Department. Results and discussion. Scientific organizations were ranked according to six scientometric indicators that describe the share distribution and quantitative characteristics of medical topic clusters. The study determined the leaders among all the 15 organizations as well as a top three among research institutes and scientific and practical centers. Topic clusters were analyzed based on the maximum and minimum values in the number of publications and citation indexes across 15 organizations and separately for two groups (research institutes and scientific and practical centers). Conclusion. One of the primary paths of future development and implementation of the methods presented in this paper appears to be the use of matrix analysis to identify and determine the key features of both individual subject clusters and joint thematic clusters. With the use of matrix analysis, we will be able to establish the most extensive, referenced, and relevant topic clusters based on the quantitative and equity ratios that we calculate not only between the topic clusters themselves but also between organizations that have publications in these clusters.