This paper is in the following e-collection/theme issue:

Natural Language Processing (873) Advanced Data Analytics in eHealth (197) Electronic Health Records (1278) Machine Learning (1984) Methods and Instruments in Medical Informatics (182) Quality Improvement (287) Research Infrastructures and Registries (76) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (209)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58977, first published .
Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study

Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study

Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study

Authors of this article:

Tomohiro Nishiyama1 Author Orcid Image ;   Ayane Yamaguchi2 Author Orcid Image ;   Peitao Han1 Author Orcid Image ;   Lis Weiji Kanashiro Pereira3 Author Orcid Image ;   Yuka Otsuki1 Author Orcid Image ;   Gabriel Herman Bernardim Andrade1 Author Orcid Image ;   Noriko Kudo1 Author Orcid Image ;   Shuntaro Yada1 Author Orcid Image ;   Shoko Wakamiya1 Author Orcid Image ;   Eiji Aramaki1 Author Orcid Image ;   Masahiro Takada2, 4 Author Orcid Image ;   Masakazu Toi5 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. Yanagisawa Y, Watabe S, Yokoyama S, Sayama K, Kizaki H, Tsuchiya M, Imai S, Someya M, Taniguchi R, Yada S, Aramaki E, Hori S. Identifying Adverse Events in Outpatients With Prostate Cancer Using Pharmaceutical Care Records in Community Pharmacies: Application of Named Entity Recognition. JMIR Cancer 2025;11:e69663 View
  2. Zitu M, Owen D, Manne A, Wei P, Li L. Large Language Models for Adverse Drug Events: A Clinical Perspective. Journal of Clinical Medicine 2025;14(15):5490 View