EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases.
epidemiology
health services research
outcomes research
Journal
Annals of the rheumatic diseases
ISSN: 1468-2060
Titre abrégé: Ann Rheum Dis
Pays: England
ID NLM: 0372355
Informations de publication
Date de publication:
01 2020
01 2020
Historique:
received:
10
05
2019
revised:
07
06
2019
accepted:
07
06
2019
pubmed:
24
6
2019
medline:
21
4
2020
entrez:
24
6
2019
Statut:
ppublish
Résumé
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). 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. 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. These EULAR points to consider discuss essential issues and provide a framework for the use of big data in RMDs.
Sections du résumé
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.
Identifiants
pubmed: 31229952
pii: annrheumdis-2019-215694
doi: 10.1136/annrheumdis-2019-215694
doi:
Types de publication
Consensus Development Conference
Guideline
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
69-76Commentaires et corrections
Type : CommentIn
Informations de copyright
© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: LG has published a study for which Orange IMT (telecommunications company) performed machine-learning analyses, without charge to the author. HS is an employee of Sanoïa, Digital CRO providing clinical research services including data science. RC is an employee of Orange Healthcare. There are no competing interests for the other authors.