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

Qingyu Chen 1, PhD;  Alex Rankine 1, 2;  Yifan Peng 1, 3, PhD;  Elaheh Aghaarabi 1, 4, MSc;  Zhiyong Lu 1, PhD

1 National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , Bethesda, MD, US

2 Harvard College , Cambridge, MA, US

3 Weill Cornell Medicine , New York, NY, US

4 Towson University , Towson, MD, US

Corresponding Author:

  • Zhiyong Lu, PhD
  • National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health
  • 8600 Rockville Pike
  • Bethesda, MD
  • US
  • Phone: 1 301 594 7089
  • Email: luzh@ncbi.nlm.nih.gov