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
Background: Natural language processing (NLP) models such as bidirectional encoder representations from transformers (BERT) hold promise in revolutionizing disease identification from electronic health records (EHRs) by potentially enhancing efficiency and accuracy. However, their practical application in practice settings demands a comprehensive and multidisciplinary approach to development and validation. The COVID-19 pandemic highlighted challenges in disease identification due to limited testing availability and challenges in handling unstructured data. In the Netherlands, where general pr... Mehr ...
Verfasser: |
Homburg, Maarten
Meijer, Eline
Berends, Matthijs
Kupers, Thijmen
Olde Hartman, Tim
Muris, Jean
de Schepper, Evelien
Velek, Premysl
Kuiper, Jeroen
Berger, Marjolein
Peters, Lilian |
Dokumenttyp: |
Artikel |
Erscheinungsdatum: |
2023 |
Reihe/Periodikum: |
Homburg , M , Meijer , E , Berends , M , Kupers , T , Olde Hartman , T , Muris , J , de Schepper , E , Velek , P , Kuiper , J , Berger , M & Peters , L 2023 , ' 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 ' , Journal of Medical Internet Research , vol. 25 , e49944 . https://doi.org/10.2196/49944 |
Sprache: |
Englisch |
Permalink: |
https://search.fid-benelux.de/Record/base-27447307 |
Datenquelle: |
BASE;
Originalkatalog |
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BASE |
Link(s) :
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https://hdl.handle.net/11370/e26c7bc4-cc98-4a6b-9c08-a79e19e65df8 |