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

Clinical Informatics (1483) Natural Language Processing (991) Electronic Health Records (1411) Generative Language Models Including ChatGPT (876)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78681, first published .
Evaluating Encoder and Decoder Models for Extended Clinical Concept Recognition in Japanese Clinical Texts: Comparative Study With Weighted Soft Matching

Evaluating Encoder and Decoder Models for Extended Clinical Concept Recognition in Japanese Clinical Texts: Comparative Study With Weighted Soft Matching

Evaluating Encoder and Decoder Models for Extended Clinical Concept Recognition in Japanese Clinical Texts: Comparative Study With Weighted Soft Matching

Authors of this article:

Yuya Tsukiji1 Author Orcid Image ;   Satoshi Kataoka1 Author Orcid Image ;   Masafumi Itokazu1 Author Orcid Image ;   Ryozo Nagai2 Author Orcid Image ;   Takeshi Imai1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Yuya Tsukiji   1 , MMSc ;   Satoshi Kataoka   1 , MS ;   Masafumi Itokazu   1 , MD ;   Ryozo Nagai   2 , MD, PhD ;   Takeshi Imai   1 , PhD

1 Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

2 Jichi Medical University, Tochigi, Japan

Corresponding Author:

  • Takeshi Imai, PhD
  • Center for Disease Biology and Integrative Medicine
  • Graduate School of Medicine, The University of Tokyo
  • 7-3-1 Hongo, Bunkyo-ku, Clinical Research Center A646, The University of Tokyo Hospital
  • Tokyo 1138655
  • Japan
  • Phone: 81 03-5841-3454
  • Email: imai@m.u-tokyo.ac.jp