Open Access
Issue |
Med Sci (Paris)
Volume 37, Number 3, Mars 2021
|
|
---|---|---|
Page(s) | 271 - 276 | |
Section | Repères | |
DOI | https://doi.org/10.1051/medsci/2021016 | |
Published online | 19 March 2021 |
- Arrêté du 21 avril 2020 complétant l’arrêté du 23 mars 2020 prescrivant les mesures d’organisation et de fonctionnement du système de santé nécessaires pour faire face à l’épidémie de covid-19 dans le cadre de l’état d’urgence sanitaire. https://www.legifrance.gouv.fr/loda/id/JORFTEXT000041812657/. [Google Scholar]
- Rapport Health Data Hub, mission de préfiguration. https://solidarites-sante.gouv.fr/IMG/pdf/181012_-_rapport_health_data_hub.pdf. [Google Scholar]
- Kruse CS, Goswamy R, Raval Y, Marawi S. Challenges and opportunities of big data in health care: a systematic review. JMIR Med Inform 2016 ; 4 : e38. [Google Scholar]
- Le L, Wang X, Carneiro G, Yang L, Eds. Deep learning and convolutional neural networks for medical imaging and clinical informatics. Springer Nature Switzerland AG, 2019 : 462 p. [Google Scholar]
- Leung TI, Dumontier M. FAIR Principles for clinical practice guidelines in a learning health system. Stud Health Technol Inform 2019 ; 264 : 1690–1691. [Google Scholar]
- Da Silva Santos LOB, Wilkinson MD, Kuzniar A, Kaliyaperumal R. FAIR data points supporting big data interoperability. In: Zelm M, Doumeingts G, Mendonça JP, eds. Enterprise interoperability in the digitized and networked factory of the future. Londres : ISTE Press Editors, 2016 : 270–9. [Google Scholar]
- Bates DW. Commentary: the role of ‘technovigilance’ in improving care in hospitals. Milbank Q 2013 ; 91 : 455–458. [Google Scholar]
- Cabitza F, Zeitoun JD. The proof of the pudding: in praise of a culture of real-world validation for medical artificial intelligence. Ann Transl Med 2019 ; 7 : 161. [Google Scholar]
- Madec J, Bouzillé G, Riou C, et al. eHOP clinical data warehouse: from a prototype to the creation of an inter-regional clinical data centers network. Stud Health Technol Inform 2019 ; 264 : 1536–1537. [Google Scholar]
- Heudel P, Livartowski A, Arveux P, et al. The ConSoRe project supports the implementation of big data in oncology. Bull Cancer (Paris) 2016 ; 103 : 949–950. [Google Scholar]
- Jannot AS, Zapletal E, Avillach P, et al. The Georges Pompidou university hospital clinical data warehouse: a 8-years follow-up experience. Int J Med Inf 2017 ; 102 : 21–28. [Google Scholar]
- Kogan NE, Clemente L, Liautaud P, et al. An Early warning approach to monitor covid-19 activity with multiple digital traces in near real-time. ArXiv juillet 2020. [Google Scholar]
- Poirier C, Lavenu A, Bertaud V, et al. Real time influenza monitoring using hospital big data in combination with machine learning methods: comparison study. JMIR Public Health Surveill 2018 ; 4 : e11361. [CrossRef] [PubMed] [Google Scholar]
- Weber GM, Mandl KD, Kohane SI. Finding the missing link for big biomedical data. JAMA 2014 ; 311 : 2479–2480. [PubMed] [Google Scholar]
- Zins M, Cuggia M, Goldberg M. Les données de santé en France. Abondantes mais complexes. Med Sci (Paris) 2021; 37 : 179–84. [EDP Sciences] [PubMed] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.