Topic Extraction of Research Seeds by Natural Language Processing From Programs of Annual Scientific Meetings of the Japanese Circulation Society.

BACKGROUND: Manually tracking research trends in extensive conference programs is challenging, so we used a natural language processing approach to automatically extract trending topics from PDF-formatted programs of the Japanese Circulation Society (JCS). METHODS AND RESULTS: Programs from JCS2023 to JCS2026 were analyzed by GiNZA and Latent Dirichlet Allocation. Among the 42,400 extracted text blocks, there were 8 primary research themes, including sustained interests in coronary artery syndrome and heart failure, an increase in interprofessional collaboration and emerging clusters in arrhythmia and valvular intervention. CONCLUSIONS: The automated workflow successfully visualized evolving academic trends, providing a robust tool for comprehensive research exploration.