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 (135) Clinical Informatics (1050) Ontologies, Classifications, and Coding (243) Electronic Health Records (1068) Machine Learning (1501) Natural Language Processing (706)

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 (8) Tweetations (3) Metrics

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, United States

2 Harvard College, Cambridge, MA, United States

3 Weill Cornell Medicine, New York, NY, United States

4 Towson University, Towson, MD, United States

Corresponding Author:

  • Zhiyong Lu, PhD
  • National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health
  • 8600 Rockville Pike
  • Bethesda, MD, 20894
  • United States
  • Phone: 1 301 594 7089
  • Email: luzh@ncbi.nlm.nih.gov