ArticleJMIR medical informatics2022
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.
Article in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 12 citations in OpenAlex.
- Predictive models for mortality readmission events and cardiovascular complications in patients with COPD: a systematic review and meta-analysis.ERJ open research · 2026Article
- Scalable and Privacy-Conscious End-to-End Processing of Large-Scale Clinical Data for Precision Medicine: Empirical Evaluation Study.JMIR medical informatics · 2026Article
- Development and External Validation of a Machine Learning Model for 90-Day Readmission in Hospitalized Older Patients with AECOPD: A Two-Center Study.International journal of chronic obstructive pulmonary disease · 2026Article
- Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine.Transfusion · 2025Review
- Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group models.Frontiers in artificial intelligence · 2025Review
- Privacy-preserving federated machine learning on FAIR health data: A real-world application.Computational and structural biotechnology journal · 2024Article
- The societal impact of Open Science: a scoping review.Royal Society open science · 2024Article
- Conceptual framework and documentation standards of cystoscopic media content for artificial intelligence.Journal of biomedical informatics · 2023Article
- Article
- Conceptual Framework and Documentation Standards of Cystoscopic Media Content for Artificial Intelligence.ArXiv · 2023Article
- FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research.Open research Europe · 2022Article
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Authors and funding
17 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOwing to the nature of health data, their sharing and reuse for research are limited by legal, technical, and ethical implications. In this sense, to address that challenge and facilitate and promote the discovery of scientific knowledge, the Findable, Accessible, Interoperable, and Reusable (FAIR) principles help organizations to share research data in a secure, appropriate, and useful way for other researchers.
objectiveThe objective of this study was the FAIRification of existing health research data sets and applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations. The entire FAIR4Health solution was validated through the assessment of a federated model for real-time prediction of 30-day readmission risk in patients with chronic obstructive pulmonary disease (COPD).
methodsThe application of the FAIR principles on health research data sets in 3 different health care settings enabled a retrospective multicenter study for the development of specific federated machine learning models for the early prediction of 30-day readmission risk in patients with COPD. This predictive model was generated upon the FAIR4Health platform. Finally, an observational prospective study with 30 days follow-up was conducted in 2 health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective studies.
resultsClinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified data sets from different health research performing organizations. The federated model for predicting the 30-day hospital readmission risk was trained using retrospective data from 4.944 patients with COPD. The assessment of the predictive model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients during the observational prospective study, which was executed from April 2021 to September 2021. Significant accuracy (0.98) and precision (0.25) of the predictive model generated upon the FAIR4Health platform were observed. Therefore, the generated prediction of 30-day readmission risk was confirmed in 87% (87/100) of cases.
conclusionsImplementing a FAIR data policy in health research performing organizations to facilitate data sharing and reuse is relevant and needed, following the discovery, access, integration, and analysis of health research data. The FAIR4Health project proposes a technological solution in the health domain to facilitate alignment with the FAIR principles.
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