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) Natural Language Processing (692)

Published on in Vol 9, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22795, first published .
Adapting Bidirectional Encoder Representations from Transformers (BERT) to Assess Clinical Semantic Textual Similarity: Algorithm Development and Validation Study

Adapting Bidirectional Encoder Representations from Transformers (BERT) to Assess Clinical Semantic Textual Similarity: Algorithm Development and Validation Study

Adapting Bidirectional Encoder Representations from Transformers (BERT) to Assess Clinical Semantic Textual Similarity: Algorithm Development and Validation Study

Authors of this article:

Klaus Kades1, 2 Author Orcid Image ;   Jan Sellner1, 3 Author Orcid Image ;   Gregor Koehler1 Author Orcid Image ;   Peter M Full1, 4 Author Orcid Image ;   T Y Emmy Lai1, 5 Author Orcid Image ;   Jens Kleesiek1, 2, 3, 6 Author Orcid Image ;   Klaus H Maier-Hein1, 2, 3, 4 Author Orcid Image
Article Authors Cited by (14) Tweetations (4) Metrics

Altmetric

Altmetric discovers Social Media mentions. Click the ‘See more details’ link for a full report.

Dimensions

Dimensions discovers Citations. Click the ‘details’ link for a full report.

Metrics Since Publication

Download .csv
Daily | Monthly | Annually