Federated Learning
The STRATA-FIT project aims to address the challenge of Difficult-to-Treat Rheumatoid Arthritis (D2T RA), a condition that affects around 20% of RA patients who do not respond well to treatment. This condition leads to a significant socio-economic burden and has been identified as an unmet medical need by the EULAR task force.
The project will utilize electronic health records (EHR) from multiple rheumatology centers across Europe to develop computational models for identifying and stratifying D2T RA patients. These models will analyze various patient data points, such as treatment response, disease activity, and comorbidities, to create personalized treatment recommendations. By leveraging EHRs, the project ensures a real-world, patient-centered approach to addressing D2T RA.
A key innovation in STRATA-FIT is the use of federated learning (FL), a privacy-preserving method of decentralized data analysis. This allows the project to securely integrate and analyze health data from different centers without transferring sensitive patient data to a central location. Instead, FL enables the analysis of local data while keeping it secure, protecting patient privacy, and ensuring compliance with legal and ethical standards. This is facilitated through the health train approach, where a “train” (or analysis) moves between data stations, allowing for iterative machine learning without compromising data security.
Through this approach, STRATA-FIT will create the first Learning Healthcare System (LHS) for RA in Europe, enabling continuous improvement in RA management. The project will also build a European biobank of D2T RA phenotypes linked to clinical data, further enhancing research into the underlying mechanisms of the disease.
In its first phase, STRATA-FIT will harmonize health data from EHRs and registries, developing models to identify D2T RA patients, stratify them into clinical phenotypes, and predict those at risk. In the second phase, these models will be integrated into a decision aid for personalized treatment, with pilot studies evaluating its feasibility and cost-effectiveness.
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This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement no. 101080243 from the Swiss State Secretariat for Education, Research and Innovation (SERI) and from Hungary’s National Research, Development and Innovation (NRDI) Fund.
Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.