Institut Pierre Louis d'Epidémiologie et de Santé Publique, INSERM, Sorbonne Universite, Paris, France
APHP, Rheumatology Department, Pitie Salpetriere Hospital, Paris, France.
Ann Rheum Dis. 2020 Jan;79(1):69-76. doi: 10.1136/annrheumdis-2019-215694. Epub 2019 Jun 22.
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.
大数据和人工智能的最近扩展为健康研究带来了巨大的机会。然而，这是一个新兴领域，需要提出最佳使用和实施的建议。这些欧洲抗风湿病联盟（EULAR）需要考虑的要点旨在指导在风湿和肌肉骨骼疾病（RMD）中大数据的收集、分析和使用。
一个由 14 名国际专家组成的多学科工作组，具有计算机科学和人工智能等一系列学科的专业知识。根据对 RMD 中大数据和其他医学领域的现状的文献回顾，制定了需要考虑的要点。分配了证据水平和建议强度，并计算了工作组成员的平均一致性水平。
制定了三个总体原则和 10 个需要考虑的要点。这些总体原则涉及处理 RMD 中大数据的道德和一般原则。这些要点涵盖了数据源和数据收集、隐私设计、数据平台、数据共享和数据分析等方面，特别是通过人工智能和机器学习。此外，这些要点指出大数据是一个不断发展的领域，需要充分报告方法和基准测试，仔细解释数据并在临床实践中实施。
这些 EULAR 需要考虑的要点讨论了基本问题，并为 RMD 中的大数据提供了一个框架。
