Imagine if hospitals could share medical secrets to find cures without actually swapping any patient files. It's like a group of chefs collaborating on a recipe by sharing the results of their cooking, but never letting anyone enter their private kitchens. This helps doctors spot early signs of brain and gut diseases by looking at patterns across thousands of people securely.
Medical data is trapped in silos due to strict privacy laws, making it impossible to analyze large-scale multimodal data for complex diseases. This delays diagnosis and increases the economic burden of neurodegenerative disorders.
A digital architecture for federated data processing and semantic integration. It includes a FAIR-compliant Data Management Plan and an ethics playbook for GDPR compliance.
If you are a drug discovery firm dealing with fragmented patient data for Parkinson's or ALS — this project developed a federated analytics system that identifies new therapy targets without moving sensitive data from hospitals. This accelerates the identification of patient subgroups for stratified trials.
If you are a medical AI software provider dealing with strict GDPR and AI Act compliance — this project developed a trustworthy digital architecture that co-trains models on distributed data. This allows you to scale AI tools across 11 countries without risking data breaches.
If you are a private hospital network dealing with high costs of neurodegenerative disease care — this project developed visual analytics and decision tools that enable earlier detection. This helps in lowering long-term care costs and improving patient quality of life.
Based on available project data, there is no information regarding the commercial pricing or cost of the system.
Yes, the project aims to build a scalable platform for federated data linkage across hospitals and biobanks, specifically designed for cross-border use within the European Health Data Space.
Based on available project data, specific licensing terms are not provided, though the project emphasizes open knowledge graphs and FAIR-compliant data management.
The system is built to be compliant with GDPR and the AI Act, using a federated architecture where sensitive data remains on-site at the source.
The project period runs from 2024-01-01 to 2027-12-31, with initial foundations and governance established in the first 18 months.
It uses semantic data integration to link clinical, genomic, imaging, signal, and text streams from distributed sources.
The consortium is heavily academic, with 10 universities and 4 research institutes, but includes 2 industry partners (11% ratio). The broad geographic spread across 11 countries suggests a strong focus on cross-border interoperability and regulatory alignment, which is critical for any health-tech product targeting the European market.
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