Analyzing fulminant myocarditis research trends and characteristics using the follower-leading clustering algorithm...

Area of Science:

• Cardiology and Clinical Medicine
• Public Health and Nursing

Background:

• Fulminant myocarditis (FM) is a severe, life-threatening cardiac condition with a high fatality rate.
• Limited research exists on FM characteristics, particularly using cluster analysis.
• This study addresses this gap by exploring FM themes and trends.

Purpose of the Study:

• To introduce and utilize a novel clustering algorithm for analyzing FM research.
• To generate a dual map and timeline view of FM themes.
• To enhance the understanding of FM characteristics and research development.

Main Methods:

• Bibliometric data from the Web of Science database (1989-2022) were analyzed.
• The study employed descriptive analytics, including CJAL scores for influential entities and FLCA for author collaborations.
• Visualizations such as dual maps and timeline views were generated using the FLCA algorithm.

Main Results:

• The United States, Huazhong University of Science and Technology (China), and Enrico Ammirati (Italy) were identified as prominent entities.
• A dual map revealed citation links between clinical medicine/surgery and general health/public/nursing research areas.
• A timeline view on Google Maps showcased themes from the top 100 cited FM articles.

Conclusions:

• A new FLCA algorithm was successfully applied to bibliometric data on FM.
• The analysis provides valuable insights into thematic trends and characteristics of FM research.
• This research can guide future endeavors and promote advancements in FM studies.