TY - JOUR
T1 - EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases
AU - Gossec, Laure
AU - Kedra, Joanna
AU - Servy, Hervé
AU - Pandit, Aridaman
AU - Stones, Simon
AU - Berenbaum, Francis
AU - Finckh, Axel
AU - Baraliakos, Xenofon
AU - Stamm, Tanja A.
AU - Gomez-Cabrero, David
AU - Pristipino, Christian
AU - Choquet, Remy
AU - Burmester, Gerd R.
AU - Radstake, Timothy R.D.J.
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-16
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
UR - https://ard.bmj.com/lookup/doi/10.1136/annrheumdis-2019-215694
UR - http://www.scopus.com/inward/record.url?scp=85068004217&partnerID=8YFLogxK
U2 - 10.1136/annrheumdis-2019-215694
DO - 10.1136/annrheumdis-2019-215694
M3 - Article
C2 - 31229952
SN - 1468-2060
VL - 79
SP - 69
EP - 76
JO - Annals of the Rheumatic Diseases
JF - Annals of the Rheumatic Diseases
IS - 1
ER -