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Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions.
McMaster, Christopher; Chan, Julia; Liew, David F L; Su, Elizabeth; Frauman, Albert G; Chapman, Wendy W; Pires, Douglas E V.
Afiliação
  • McMaster C; Department of Clinical Pharmacology & Therapeutics, Austin Health, Melbourne, Victoria, Australia; Department of Rheumatology, Austin Health, Melbourne, Victoria, Australia; The Centre for Digital Transformation of Health, University of Melbourne, Melbourne, Victoria, Australia; School of Comput
  • Chan J; Department of Rheumatology, Austin Health, Melbourne, Victoria, Australia.
  • Liew DFL; Department of Clinical Pharmacology & Therapeutics, Austin Health, Melbourne, Victoria, Australia; Department of Rheumatology, Austin Health, Melbourne, Victoria, Australia; Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.
  • Su E; Department of Clinical Pharmacology & Therapeutics, Austin Health, Melbourne, Victoria, Australia.
  • Frauman AG; Department of Clinical Pharmacology & Therapeutics, Austin Health, Melbourne, Victoria, Australia; Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.
  • Chapman WW; The Centre for Digital Transformation of Health, University of Melbourne, Melbourne, Victoria, Australia.
  • Pires DEV; The Centre for Digital Transformation of Health, University of Melbourne, Melbourne, Victoria, Australia; School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia.
J Biomed Inform ; 137: 104265, 2023 01.
Article em En | MEDLINE | ID: mdl-36464227
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos / Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: J Biomed Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos / Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: J Biomed Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article