A visual analysis of domestic county medical community research based on clustering algorithms
Guoqin Zhang, Haiyang Nie · 2023
To systematically explore the research hotspots and frontier trends in the field of data mining then domestic county medical community, and to provide data reference for future in-depth research in this field. Co-occurrence analysis, mutual information algorithms, LLR algorithms and LSI algorithms, etc. were used to retrieve and summarize the literature collected from 2016 to 2023 using the CNKI database as the data source, to draw the knowledge map and to perform visualization and analysis. The amount of literature on county medical community has increased rapidly from 2018 onwards, and county medical community have become a widely followed field. The collaborative network among authors is relatively loose, and no core group of authors has been formed yet. The research hotspots are mainly divided into three categories, which are the basic theoretical research related to medical communities, the optimal allocation of resources within medical communities, and the analysis of medical community operation models. In terms of cutting-edge trends, information construction, financial management, talent training, medical insurance payment, and medical prevention integration have become new themes and directions for research on county-level medical communities. The field of county-level medical community should further explore cross-sectoral and cross-regional collaborative research mechanisms and strengthen cross-collaboration in different fields. In the future, we should build a big data information system to support existing research, as well as deepen the theory of county-level medical communities by conducting diverse collaborative research models and practical theories combined with research methods.