Forty-year evolution of research topics in the Journal of Yeungnam Medical Science: a text mining and keyword network...

This study investigated long-term publication trends and thematic evolution in the Journal of Yeungnam Medical Science (JYMS) using bibliometric and text mining approaches.

Publication metadata from JYMS articles published between 1986 and 2025 were analyzed. Author keywords were preprocessed through normalization and stopword removal. Furthermore, keyword frequency analysis, word clouds, trajectory heatmaps, thematic evolution analysis, burst keyword analysis, and keyword co-occurrence network analysis were conducted using Python-based bibliometric methods.

A total of 1,760 articles were analyzed. The average number of keywords per article increased from 1.5±1.7 (1986-1995) to 3.8±1.4 (2016-2025). Earlier decades were characterized by keywords related to pregnancy, psychiatry, and disease-specific topics, whereas recent years demonstrated the increasing prominence of imaging-, metabolic-, and outcome-related research. Coronavirus disease 2019 emerged as the most prominent recent burst keyword. Co-occurrence network analysis demonstrated increasing interdisciplinary connectivity, with imaging-related keywords occupying central network positions.

Over the past four decades, JYMS publications have demonstrated progressive thematic diversification and increased interdisciplinary integration. Bibliometric and text mining analyses may provide valuable information on the journal's future research directions and academic development.