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[Can Big Data change our practices?] / Le Big Data peut-il changer nos pratiques ?
Daien, V; Muyl-Cipollina, A.
Afiliação
  • Daien V; Service d'ophtalmologique, hôpital Gui De Chauliac, 80, avenue Augustin Fliche, 34295 Montpellier, France; Inserm, epidemiological and clinical research, université Montpellier, 34295 Montpellier, France; The Save Sight Institute, Sydney Medical School, The University of Sydney, Sydney, Australie.
  • Muyl-Cipollina A; Service d'ophtalmologique, hôpital Gui De Chauliac, 80, avenue Augustin Fliche, 34295 Montpellier, France. Electronic address: a.cipollinamuyl@gmail.com.
J Fr Ophtalmol ; 42(6): 551-571, 2019 Jun.
Article em Fr | MEDLINE | ID: mdl-30979558
ABSTRACT
The European Medicines Agency has defined Big Data by the "3 V's" Volume, Velocity and Variety. These large databases allow access to real life data on patient care. They are particularly suited for studies of adverse events and pharmacoepidemiology. Deep learning is a collection of algorithms used in machine learning, used to model high-level abstractions in data using model architectures, which are composed of multiple nonlinear transformations. This article shows how Big Data and Deep Learning can help in ophthalmology, pointing out their advantages and disadvantages. A literature review is presented in this article illustrating the uses of Deep Learning in ophthalmology.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Oftalmologia / Algoritmos / Bases de Dados Factuais / Aprendizado de Máquina / Big Data Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: Fr Revista: J Fr Ophtalmol Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Austrália

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Oftalmologia / Algoritmos / Bases de Dados Factuais / Aprendizado de Máquina / Big Data Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: Fr Revista: J Fr Ophtalmol Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Austrália