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) Methods and Instruments in Medical Informatics (164) Clinical Informatics (1160) Ontologies, Classifications, and Coding (270) Electronic Health Records (1181) Machine Learning (1730) Natural Language Processing (801)

Published on in Vol 9, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27386, first published .
Benchmarking Effectiveness and Efficiency of Deep Learning Models for Semantic Textual Similarity in the Clinical Domain: Validation Study

Benchmarking Effectiveness and Efficiency of Deep Learning Models for Semantic Textual Similarity in the Clinical Domain: Validation Study

Benchmarking Effectiveness and Efficiency of Deep Learning Models for Semantic Textual Similarity in the Clinical Domain: Validation Study

Authors of this article:

Qingyu Chen1 Author Orcid Image ;   Alex Rankine1, 2 Author Orcid Image ;   Yifan Peng1, 3 Author Orcid Image ;   Elaheh Aghaarabi1, 4 Author Orcid Image ;   Zhiyong Lu1 Author Orcid Image
Article Authors Cited by (9) Tweetations (3) Metrics

Journals

  1. Chen Q, Du J, Allot A, Lu Z. LitMC-BERT: Transformer-Based Multi-Label Classification of Biomedical Literature With An Application on COVID-19 Literature Curation. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2022;19(5):2584 View
  2. Chen Q, Allot A, Leaman R, Islamaj R, Du J, Fang L, Wang K, Xu S, Zhang Y, Bagherzadeh P, Bergler S, Bhatnagar A, Bhavsar N, Chang Y, Lin S, Tang W, Zhang H, Tavchioski I, Pollak S, Tian S, Zhang J, Otmakhova Y, Yepes A, Dong H, Wu H, Dufour R, Labrak Y, Chatterjee N, Tandon K, Laleye F, Rakotoson L, Chersoni E, Gu J, Friedrich A, Pujari S, Chizhikova M, Sivadasan N, VG S, Lu Z. Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations. Database 2022;2022 View
  3. Lara-Clares A, Lastra-Díaz J, Garcia-Serrano A, Qamar U. A reproducible experimental survey on biomedical sentence similarity: A string-based method sets the state of the art. PLOS ONE 2022;17(11):e0276539 View
  4. Ma S, Jiang S, Yang O, Zhang X, Fu Y, Zhang Y, Kaareen A, Ling M, Chen J, Shang C. Use of Machine Learning Tools in Evidence Synthesis of Tobacco Use Among Sexual and Gender Diverse Populations: Algorithm Development and Validation. JMIR Formative Research 2024;8:e49031 View
  5. Aygün İ, Kaya B, Kaya M. Identifying patients in need of psychological treatment with language representation models. Multimedia Tools and Applications 2024;84(1):397 View
  6. Chen Q, Hu Y, Peng X, Xie Q, Jin Q, Gilson A, Singer M, Ai X, Lai P, Wang Z, Keloth V, Raja K, Huang J, He H, Lin F, Du J, Zhang R, Zheng W, Adelman R, Lu Z, Xu H. Benchmarking large language models for biomedical natural language processing applications and recommendations. Nature Communications 2025;16(1) View

Books/Policy Documents

  1. Dramé K, Diallo G, Sambe G. Web Information Systems and Technologies. View

Conference Proceedings

  1. Faramarzi N, Dara A, Banerjee R. 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI). Combining Attention-based Models with the MeSH Ontology for Semantic Textual Similarity in Clinical Notes View
  2. Matta A, Suesserman M, McNamee D, Lasaga D, Olson D, Bowen E, Bhattacharya S. 2023 International Conference on Machine Learning and Applications (ICMLA). Embedding Representations of Diagnosis Codes for Outlier Payment Detection View