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

Kades, Klaus; Sellner, Jan; Koehler, Gregor; Full, Peter M; Lai, T Y Emmy; Kleesiek, Jens; Maier-Hein, Klaus H

Kades, Klaus; Sellner, Jan; Koehler, Gregor; Full, Peter M; Lai, T Y Emmy; Kleesiek, Jens; Maier-Hein, Klaus H.

Afiliación

Kades K; German Cancer Research Center (DKFZ), Heidelberg, Germany.
Sellner J; Partner Site Heidelberg, German Cancer Consortium (DKTK), Heidelberg, Germany.
Koehler G; German Cancer Research Center (DKFZ), Heidelberg, Germany.
Full PM; Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany.
Lai TYE; German Cancer Research Center (DKFZ), Heidelberg, Germany.
Kleesiek J; German Cancer Research Center (DKFZ), Heidelberg, Germany.
Maier-Hein KH; Heidelberg University, Heidelberg, Germany.

JMIR Med Inform ; 9(2): e22795, 2021 Feb 03.

Article en En | MEDLINE | ID: mdl-33533728

Palabras clave

National NLP Clinical Challenges; Natural Language Processing; clinical text mining; semantic textual similarity

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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: JMIR Med Inform Año: 2021 Tipo del documento: Article País de afiliación: Alemania

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