This paper is in the following e-collection/theme issue:

Artificial Intelligence (1371) JMIR Theme Issue: COVID-19 Special Issue (2452) Clinical Informatics (1022) Clinical Information and Decision Making (1407) Electronic Health Records (1053) New Methods (945) Ontologies, Classifications, and Coding (239)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49944, first published .
A Natural Language Processing Model for COVID-19 Detection Based on Dutch General Practice Electronic Health Records by Using Bidirectional Encoder Representations From Transformers: Development and Validation Study

A Natural Language Processing Model for COVID-19 Detection Based on Dutch General Practice Electronic Health Records by Using Bidirectional Encoder Representations From Transformers: Development and Validation Study

A Natural Language Processing Model for COVID-19 Detection Based on Dutch General Practice Electronic Health Records by Using Bidirectional Encoder Representations From Transformers: Development and Validation Study

Authors of this article:

Maarten Homburg1 Author Orcid Image ;   Eline Meijer1, 2 Author Orcid Image ;   Matthijs Berends1, 3, 4 Author Orcid Image ;   Thijmen Kupers1, 2 Author Orcid Image ;   Tim Olde Hartman5 Author Orcid Image ;   Jean Muris6 Author Orcid Image ;   Evelien de Schepper7 Author Orcid Image ;   Premysl Velek7 Author Orcid Image ;   Jeroen Kuiper8 Author Orcid Image ;   Marjolein Berger1 Author Orcid Image ;   Lilian Peters1, 2, 9 Author Orcid Image
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