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

Formative Evaluation of Digital Health Interventions (2491) Natural Language Processing (706) Electronic Health Records (1068) Ontologies, Classifications, and Coding (243) Machine Learning (1501) mHealth for Data Collection and Research (981) Clinical Informatics (1050) Artificial Intelligence (1421) Secondary Use of Clinical Data for Research and Surveillance (375) Development and Evaluation of Research Methods, Instruments and Tools (511)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55798, first published .
Using the Natural Language Processing System Medical Named Entity Recognition-Japanese to Analyze Pharmaceutical Care Records: Natural Language Processing Analysis

Using the Natural Language Processing System Medical Named Entity Recognition-Japanese to Analyze Pharmaceutical Care Records: Natural Language Processing Analysis

Using the Natural Language Processing System Medical Named Entity Recognition-Japanese to Analyze Pharmaceutical Care Records: Natural Language Processing Analysis

Authors of this article:

Yukiko Ohno1 Author Orcid Image ;   Riri Kato1 Author Orcid Image ;   Haruki Ishikawa1 Author Orcid Image ;   Tomohiro Nishiyama2 Author Orcid Image ;   Minae Isawa1 Author Orcid Image ;   Mayumi Mochizuki1 Author Orcid Image ;   Eiji Aramaki2 Author Orcid Image ;   Tohru Aomori3 Author Orcid Image
Article Authors Cited by (2) Tweetations (4) Metrics

Journals

  1. Herman Bernardim Andrade G, Nishiyama T, Fujimaki T, Yada S, Wakamiya S, Takagi M, Kato M, Miyashiro I, Aramaki E. Assessing domain adaptation in adverse drug event extraction on real-world breast cancer records. International Journal of Medical Informatics 2024;191:105539 View
  2. Liu S, Wang A, Xiu X, Zhong M, Wu S. Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study. JMIR Medical Informatics 2024;12:e59782 View