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

Theme Issue 2020:National NLP Clinical Challenges/Open Health Natural Language Processing 2019 Challenge Selected Papers (17) Machine Learning (1934) Natural Language Processing (860)

Published on in Vol 9, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23099, first published .
Predicting Semantic Similarity Between Clinical Sentence Pairs Using Transformer Models: Evaluation and Representational Analysis

Predicting Semantic Similarity Between Clinical Sentence Pairs Using Transformer Models: Evaluation and Representational Analysis

Predicting Semantic Similarity Between Clinical Sentence Pairs Using Transformer Models: Evaluation and Representational Analysis

Authors of this article:

Mark Ormerod1 Author Orcid Image ;   Jesús Martínez del Rincón1 Author Orcid Image ;   Barry Devereux1 Author Orcid Image
Article Authors Cited by (20) Tweetations (3) Metrics

Journals

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  6. Liu M, Liu Y, Shi W, Lou Y, Sun Y, Meng Q, Wang D, Xu F, Zhang Y, Zhang L, Leng J. EMPT: a sparsity Transformer for EEG-based motor imagery recognition. Frontiers in Neuroscience 2024;18 View
  7. Sblendorio E, Dentamaro V, Lo Cascio A, Germini F, Piredda M, Cicolini G. Integrating human expertise & automated methods for a dynamic and multi-parametric evaluation of large language models’ feasibility in clinical decision-making. International Journal of Medical Informatics 2024;188:105501 View
  8. Xu F, Liu M, Chen X, Yan Y, Zhao J, Liu Y, Zhao J, Pang S, Yin S, Leng J, Zhang Y. Time–frequency–space transformer EEG decoding for spinal cord injury. Cognitive Neurodynamics 2024;18(6):3491 View
  9. Chen Y, Zhang C, Bai R, Sun T, Ding W, Wang R. A review of medical text analysis: Theory and practice. Information Fusion 2025;119:103024 View
  10. Kang S, Park H, Taira R, Kim H. Detecting Redundant Health Survey Questions by Using Language-Agnostic Bidirectional Encoder Representations From Transformers Sentence Embedding: Algorithm Development Study. JMIR Medical Informatics 2025;13:e71687 View
  11. Benrubi L, Sato T, Westgard L, Zollo-Venecek K, Socrates B, Sweigart B, Ridgway J, Suzuki J, Morales Y, Goodman-Meza D, Wurcel A. Sensitivity and Specificity of Natural Language Processing Systems for Identification of Hospitalized People Who Use Drugs. Open Forum Infectious Diseases 2025;12(7) View

Books/Policy Documents

  1. Darji H, Mitrović J, Granitzer M. Knowledge Discovery, Knowledge Engineering and Knowledge Management. View
  2. Menad S, Laddada W, Abdeddaïm S, Soualmia L. Complex Computational Ecosystems. View
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  4. Sblendorio E, Lo Cascio A, Napolitano D, Germini F, Dentamaro V, Piredda M, Cicolini G. Computational Intelligence Methods for Bioinformatics and Biostatistics. View
  5. Aguinalde P, Shin J, Smith S, Carlo M. Artificial Intelligence in Education. View

Conference Proceedings

  1. Bhana N, van Zyl T. 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI). Knowledge Graph Fusion for Language Model Fine-Tuning View
  2. Mavrogiorgos K, Mavrogiorgou A, Kiourtis A, Zafeiropoulos N, Kleftakis S, Kyriazis D. 2022 32nd Conference of Open Innovations Association (FRUCT). Automated Rule-Based Data Cleaning Using NLP View
  3. Taru U, Patil A. 2022 International Conference on Machine Learning, Computer Systems and Security (MLCSS). Building Ontology for Toxic words View
  4. Alfianto M, Priyadi Y, Laksitowening K. 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT). Semantic Textual Similarity in Requirement Specification and Use Case Description based on Sentence Transformer Model View