The FAIR4Health consortium joins forces facilitating and encourage the EU Health Research community to share and reuse their datasets derived from publicly funded research initiatives by applying the FAIR principles (Findable, Accessible, Interoperable and Reusable); through the demonstration of the potential impact that such strategy have on health outcomes and health research. In this sense, FAIR4Health applies privacy-preserving distributed data mining techniques over the shared datasets to develop 2 pathfinder case studies: (i) supporting the discovery of disease onset triggers and disease association patterns in co-morbid patients, and (ii) a prediction service for 30-days readmission risk in complex chronic patients.
The overall objective of FAIR4Health is to facilitate and encourage the EU health research community to FAIRify, share and reuse their datasets derived from publicly funded research initiatives.
Our team has a fundamental role in the design, creation, and implementation of the technological platform of the project that applies the FAIR (Findable, Accessible, Interoperable, and Reusable) philosophy to demonstrate the great impact that this strategy entails in improving health research. FAIR4Health applies principles of distributed privacy to data mining algorithms over shared and federated databases to:
- Support medical research in the early diagnosis of diseases and their potential association with other pathologies in patients with comorbidities
- Provide a prediction service of the risk of readmission in-hospital emergencies to 30 days of patients with complex chronic diseases