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

Natural Language Processing (937) AI Language Models in Health Care (224) Case Study (35) Electronic Health Records (1354) Machine Learning (2173)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56955, first published .
Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study

Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study

Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study

Authors of this article:

Sheng-Feng Sung1, 2 Author Orcid Image ;   Ya-Han Hu3 Author Orcid Image ;   Chong-Yan Chen3 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Garcia-Lopez A, Cuervo-Rojas J, Garcia-Lopez J, Giron-Luque F, Bako A. Using Natural Language Processing and Machine Learning to classify the status of kidney allograft in Electronic Medical Records written in Spanish. PLOS One 2025;20(5):e0322587 View
  2. Liu W, Wan J, Lv N, Zhou X. Bi-term association based on fuzzy logic. Array 2026;29:100704 View