Circulation reports · 2026
Topic Extraction of Research Seeds by Natural Language Processing From Programs of Annual Scientific Meetings of the Japanese Circulation Society.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Hosaka Itaru, Akiyama Yukinori, Tanaka Marenao, Sato Tatsuya, Furuhashi Masato
Аннотация
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.
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.