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.