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Natural Language Processing (1271) Case Study (80) Electronic Health Records (1718) Machine Learning (3151) AI Language Models in Health Care (738)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56955, first published .
Doctor typing on a laptop, wearing a stethoscope

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 (5) 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
  3. Zhang C, Zhang H, Gao X, Xue Y. Biomedical WSD based on contrastive learning with focus on representative features. Biomedical Signal Processing and Control 2026;128:111282 View

Books/Policy Documents

  1. Manikandan V, Wen D, Dewan M. Artificial Intelligence in Healthcare. View

Conference Proceedings

  1. Joshi V, Nirkhi S, Guda S, Motghare M, Singh Y, Patil S. 2025 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). Disambiguation of Clinical Abbreviations in EHRs Using Fine-Tuned Biomedical BERT View