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

Natural Language Processing (775) Machine Learning (1662) Electronic Health Records (1139) Clinical Information and Decision Making (1584)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46348, first published .
Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study

Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study

Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study

Authors of this article:

Kung-Hsun Weng1 Author Orcid Image ;   Chung-Feng Liu2 Author Orcid Image ;   Chia-Jung Chen3 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. Su Y, Babore Y, Kahn C. A Large Language Model to Detect Negated Expressions in Radiology Reports. Journal of Imaging Informatics in Medicine 2024 View
  2. Yuan H. Natural Language Processing for Chest X‐Ray Reports in the Transformer Era: BERT‐Like Encoders for Comprehension and GPT‐Like Decoders for Generation. iRADIOLOGY 2025 View