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 (1465) Natural Language Processing (693)

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 (14) Tweetations (3) Metrics

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  2. Turner J, Kantardzic M, Vickers-Smith R, Brown A. Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification. JMIR Infodemiology 2023;3:e38390 View
  3. Mavrogiorgou A, Kiourtis A, Kleftakis S, Mavrogiorgos K, Zafeiropoulos N, Kyriazis D. A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions. Sensors 2022;22(22):8615 View
  4. Mo C, Yin J, Fung I, Tse Z. Aggregating Twitter Text through Generalized Linear Regression Models for Tweet Popularity Prediction and Automatic Topic Classification. European Journal of Investigation in Health, Psychology and Education 2021;11(4):1537 View
  5. Ru B, Tan X, Liu Y, Kannapur K, Ramanan D, Kessler G, Lautsch D, Fonarow G. Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study. JMIR Formative Research 2023;7:e41775 View
  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 View

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  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