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Improving Systematic Review Updates With Natural Language Processing Through Abstract Component Classification and Selection: Algorithm Development and Validation

Tatsuki Hasegawa, Hayato Kizaki, Keisho Ikegami, Shungo Imai, Yuki Yanagisawa, Shuntaro Yada, Eiji Aramaki, Satoko Hori

JMIR Med Inform 2025;13:e65371

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Identifying Adverse Events in Outpatients With Prostate Cancer Using Pharmaceutical Care Records in Community Pharmacies: Application of Named Entity Recognition

Yuki Yanagisawa, Satoshi Watabe, Sakura Yokoyama, Kyoko Sayama, Hayato Kizaki, Masami Tsuchiya, Shungo Imai, Mitsuhiro Someya, Ryoo Taniguchi, Shuntaro Yada, Eiji Aramaki, Satoko Hori

JMIR Cancer 2025;11:e69663

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Machine Learning–Based Prediction of Delirium and Risk Factor Identification in Intensive Care Unit Patients With Burns: Retrospective Observational Study

Ryo Esumi, Hiroki Funao, Eiji Kawamoto, Ryota Sakamoto, Asami Ito-Masui, Fumito Okuno, Toru Shinkai, Atsuya Hane, Kaoru Ikejiri, Yuichi Akama, Arong Gaowa, Eun Jeong Park, Ryo Momosaki, Ryuji Kaku, Motomu Shimaoka

JMIR Form Res 2025;9:e65190

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Performance Improvement of a Natural Language Processing Tool for Extracting Patient Narratives Related to Medical States From Japanese Pharmaceutical Care Records by Increasing the Amount of Training Data: Natural Language Processing Analysis and Validation Study

Similarly, Aramaki et al [26] used Japanese case reports as training data to develop a system for extracting a variety of symptoms. The F1-scores of their system were 0.87 for NER and 0.63 for NER with positive-negative classification [26], both of which were lower than the scores achieved by our system.

Yukiko Ohno, Tohru Aomori, Tomohiro Nishiyama, Riri Kato, Reina Fujiki, Haruki Ishikawa, Keisuke Kiyomiya, Minae Isawa, Mayumi Mochizuki, Eiji Aramaki, Hisakazu Ohtani