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EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases.
Gossec, Laure; Kedra, Joanna; Servy, Hervé; Pandit, Aridaman; Stones, Simon; Berenbaum, Francis; Finckh, Axel; Baraliakos, Xenofon; Stamm, Tanja A; Gomez-Cabrero, David; Pristipino, Christian; Choquet, Remy; Burmester, Gerd R; Radstake, Timothy R D J.
Afiliação
  • Gossec L; Institut Pierre Louis d'Epidémiologie et de Santé Publique, INSERM, Sorbonne Universite, Paris, France laure.gossec@gmail.com.
  • Kedra J; APHP, Rheumatology Department, Pitie Salpetriere Hospital, Paris, France.
  • Servy H; Institut Pierre Louis d'Epidémiologie et de Santé Publique, INSERM, Sorbonne Universite, Paris, France.
  • Pandit A; APHP, Rheumatology Department, Pitie Salpetriere Hospital, Paris, France.
  • Stones S; Sanoïa, e-Health services, Gardanne, France.
  • Berenbaum F; Dept of Rheumatology, Clinical Immunology and Laboratory of Translational Immunology, Universitair Medisch Centrum Utrecht, Utrecht, The Netherlands.
  • Finckh A; School of Healthcare, University of Leeds, Leeds, UK.
  • Baraliakos X; Rheumatology, St Antoine Hospital, Sorbonne Université, INSERM, Paris, France.
  • Stamm TA; Division of Rheumatology, University of Geneva, Geneva, Switzerland.
  • Gomez-Cabrero D; Rheumazentrum Ruhrgebiet Sankt Josefs-Krankenhaus, Herne, Germany.
  • Pristipino C; Ruhr-Universitat Bochum, Bochum, Germany.
  • Choquet R; Section for Outcomes Research, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
  • Burmester GR; Translational Bioinformatics Unit, Navarra Biomed, Departamento de Salud-Universidad Públicade Navarra, Pamplona, Navarra, Spain.
  • Radstake TRDJ; Ospedale San Filippo Neri, Rome, Italy.
Ann Rheum Dis ; 79(1): 69-76, 2020 01.
Article em En | MEDLINE | ID: mdl-31229952
ABSTRACT

BACKGROUND:

Tremendous opportunities for health research have been unlocked by the recent expansion of big data and artificial intelligence. However, this is an emergent area where recommendations for optimal use and implementation are needed. The objective of these European League Against Rheumatism (EULAR) points to consider is to guide the collection, analysis and use of big data in rheumatic and musculoskeletal disorders (RMDs).

METHODS:

A multidisciplinary task force of 14 international experts was assembled with expertise from a range of disciplines including computer science and artificial intelligence. Based on a literature review of the current status of big data in RMDs and in other fields of medicine, points to consider were formulated. Levels of evidence and strengths of recommendations were allocated and mean levels of agreement of the task force members were calculated.

RESULTS:

Three overarching principles and 10 points to consider were formulated. The overarching principles address ethical and general principles for dealing with big data in RMDs. The points to consider cover aspects of data sources and data collection, privacy by design, data platforms, data sharing and data analyses, in particular through artificial intelligence and machine learning. Furthermore, the points to consider state that big data is a moving field in need of adequate reporting of methods and benchmarking, careful data interpretation and implementation in clinical practice.

CONCLUSION:

These EULAR points to consider discuss essential issues and provide a framework for the use of big data in RMDs.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Reumatologia / Doenças Reumáticas / Doenças Musculoesqueléticas / Big Data Tipo de estudo: Guideline Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Reumatologia / Doenças Reumáticas / Doenças Musculoesqueléticas / Big Data Tipo de estudo: Guideline Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article