Identifying Patient Populations in Texts Describing Drug Approvals Through Deep Learning-Based Information Extraction: Development of a Natural Language Processing Algorithm.

Gendrin, Aline; Souliotis, Leonidas; Loudon-Griffiths, James; Aggarwal, Ravisha; Amoako, Daniel; Desouza, Gregory; Dimitrievska, Sashka; Metcalfe, Paul; Louvet, Emilie; Sahni, Harpreet

Gendrin, Aline; Souliotis, Leonidas; Loudon-Griffiths, James; Aggarwal, Ravisha; Amoako, Daniel; Desouza, Gregory; Dimitrievska, Sashka; Metcalfe, Paul; Louvet, Emilie; Sahni, Harpreet.

Afiliación

Gendrin A; AstraZeneca, Cambridge, United Kingdom.
Souliotis L; AstraZeneca, Cambridge, United Kingdom.
Loudon-Griffiths J; AstraZeneca, Cambridge, United Kingdom.
Aggarwal R; AstraZeneca, Bangalore, India.
Amoako D; AstraZeneca, Wilmington, DE, United States.
Desouza G; AstraZeneca, Cambridge, United Kingdom.
Dimitrievska S; AstraZeneca, Gaithersburg, MD, United States.
Metcalfe P; AstraZeneca, Cambridge, United Kingdom.
Louvet E; AstraZeneca, Cambridge, United Kingdom.
Sahni H; AstraZeneca, Wilmington, DE, United States.

JMIR Form Res ; 7: e44876, 2023 Jun 22.

Article en En | MEDLINE | ID: mdl-37347514

Palabras clave

BERT; NLP; algorithm; artificial intelligence; cancer; classification; data extraction; data mining; deep-learning; development; drug approval; free text; information retrieval; line of therapy; machine learning; natural language processing; oncology; pharmaceutic; pharmacology; pharmacy; stage of cancer; text extraction; text mining; unstructured data

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: JMIR Form Res Año: 2023 Tipo del documento: Article País de afiliación: Reino Unido

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