This paper is in the following e-collection/theme issue:

Theme Issue: Medical Informatics and COVID-19 (107) Digital Science, Open Science (79) Decision Support for Health Professionals (1210) Electronic Health Records (1068) Secondary Use of Clinical Data for Research and Surveillance (375) Machine Learning (1501)

Published on in Vol 10, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35307, first published .
Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study

Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study

Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study

Authors of this article:

Celia Alvarez-Romero1 Author Orcid Image ;   Alicia Martinez-Garcia1 Author Orcid Image ;   Jara Ternero Vega2 Author Orcid Image ;   Pablo Díaz-Jimènez2 Author Orcid Image ;   Carlos Jimènez-Juan2 Author Orcid Image ;   María Dolores Nieto-Martín2 Author Orcid Image ;   Esther Román Villarán1 Author Orcid Image ;   Tomi Kovacevic3, 4 Author Orcid Image ;   Darijo Bokan3 Author Orcid Image ;   Sanja Hromis3, 4 Author Orcid Image ;   Jelena Djekic Malbasa3, 4 Author Orcid Image ;   Suzana Beslać3 Author Orcid Image ;   Bojan Zaric3, 4 Author Orcid Image ;   Mert Gencturk5 Author Orcid Image ;   A Anil Sinaci5 Author Orcid Image ;   Manuel Ollero Baturone2 Author Orcid Image ;   Carlos Luis Parra Calderón1 Author Orcid Image
Article Authors Cited by (7) Tweetations (3) Metrics

Journals

  1. Alvarez-Romero C, Martínez-García A, Sinaci A, Gencturk M, Méndez E, Hernández-Pérez T, Liperoti R, Angioletti C, Löbe M, Ganapathy N, Deserno T, Almada M, Costa E, Chronaki C, Cangioli G, Cornet R, Poblador-Plou B, Carmona-Pírez J, Gimeno-Miguel A, Poncel-Falcó A, Prados-Torres A, Kovacevic T, Zaric B, Bokan D, Hromis S, Djekic Malbasa J, Rapallo Fernández C, Velázquez Fernández T, Rochat J, Gaudet-Blavignac C, Lovis C, Weber P, Quintero M, Perez-Perez M, Ashley K, Horton L, Parra Calderón C. FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research. Open Research Europe 2022;2:34 View
  2. Martínez-García A, Alvarez-Romero C, Román-Villarán E, Bernabeu-Wittel M, Luis Parra-Calderón C. FAIR principles to improve the impact on health research management outcomes. Heliyon 2023;9(5):e15733 View
  3. Eminaga O, Lee T, Ge J, Shkolyar E, Laurie M, Long J, Hockman L, Liao J. Conceptual framework and documentation standards of cystoscopic media content for artificial intelligence. Journal of Biomedical Informatics 2023;142:104369 View
  4. Sinaci A, Gencturk M, Alvarez-Romero C, Laleci Erturkmen G, Martinez-Garcia A, Escalona-Cuaresma M, Parra-Calderon C. Privacy-preserving federated machine learning on FAIR health data: A real-world application. Computational and Structural Biotechnology Journal 2024;24:136 View
  5. Cole N, Kormann E, Klebel T, Apartis S, Ross-Hellauer T. The societal impact of Open Science: a scoping review. Royal Society Open Science 2024;11(6) View
  6. Eradat Oskoui S, Retford M, Forde E, Barnes R, J Hunter K, Wozencraft A, Thompson S, Orton C, Ford D, Heys S, Kennedy J, McNerney C, Peng J, Ghanbariadolat H, Rees S, H Mulholland R, Sheikh A, Burgner D, Brockway M, B. Azad M, Rodriguez N, Zoega H, J Stock S, Calvert C, E Miller J, Fiorentino N, Racine A, Haggstrom J, Postlethwaite N. Developing a prototype for federated analysis to enhance privacy and enable trustworthy access to COVID-19 research data. International Journal of Medical Informatics 2025;195:105708 View
  7. Li N, Lewin A, Ning S, Waito M, Zeller M, Tinmouth A, Shih A. Privacy‐preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine. Transfusion 2024 View