Terminologias e dados em mundo real

EM-BRA-INM-0034

Referências:
1. von Gerich H, Chomutare T, Kytö V, Lundberg P, Siggaard T, Peltonen LM. A rapid review on the application of common data models in healthcare: Recommendations for data governance and federated learning in artificial intelligence development. Digit Health. 2025;11:20552076251395536. 
2. Blacketer C, Schuemie MJ, Moinat M, Voss EA, Camprubi M, Rijnbeek PR, et al. Advancing Real-World Evidence Through a Federated Health Data Network (EHDEN): descriptive study. J Med Internet Res. 2025;27:e74119. 
3. Austin JA, Lobo EH, Samadbeik M, Engstrom T, Philip R, Pole JD, et al. Decades in the making: the evolution of digital health research infrastructure through synthetic data, common data models, and federated learning. J Med Internet Res. 2024;26:e58637. 
4. Facile R, Muhlbradt EE, Gong M, Li Q, Popat V, Pétavy F, et al. Use of Clinical Data Interchange Standards Consortium (CDISC) standards for real-world data: expert perspectives from a qualitative delphi survey. JMIR Med Inform. 2022;10(1):e30363. 
5. Rivera DR, Cutler TL, McShane L, Meeker-O'Connell A, Marston HD, Califf RM, et al. Modernizing research and evidence consensus definitions: a food and drug administration-national institutes of health collaboration. JAMA Netw Open. 2025;8(6):e2516674. 
6. Zhang S, Cornet R, Benis N. Cross-standard health data harmonization using semantics of data elements. Sci Data. 2024;11(1):1407. 
7. Zhang F, Kreuter D, Chen Y, Dittmer S, Tull S, Shadbahr T; BloodCounts! consortium; Preller J, Rudd JHF, Aston JAD, Schönlieb CB, Gleadall N, Roberts M. Recent methodological advances in federated learning for healthcare. Patterns (N Y). 2024;5(6):101006. 
8. Park HA. Why terminology standards matter for data-driven artificial intelligence in healthcare. Ann Lab Med. 2024;44(6):467–71. 
9. Frayling L, Bharat SS, Pattinson E, Stock J, Lugg-Widger F, Gordon E, et al. A review of synthetic data terminology for privacy preserving use cases. Int J Popul Data Sci. 2025;10(2):2967. 
10. Barbereau T, Delgado Fernandez J, Potenciano Menci S. The governance of federated learning: a decision framework for organisational archetypes. Data & Policy. 2025;7:e53. 
11. Shenoy D, Bhat R, Krishna Prakasha K. Exploring privacy mechanisms and metrics in federated learning. Artif Intell Ver. 2025;58:223.