
PENUMBRA EUROPE GMBH
PENUMBRA EUROPE GMBH
1 Projects, page 1 of 1
Open Access Mandate for Publications and Research data assignment_turned_in Project2024 - 2029Partners:DSES, JANSSEN CILAG, Agostino Gemelli University Polyclinic, KUL, EURECAT +19 partnersDSES,JANSSEN CILAG,Agostino Gemelli University Polyclinic,KUL,EURECAT,STICHTING AMSTERDAM UMC,TEAMIT RESEARCH SL,SAFE,ASTRAZENECA FARMACEUTICA SPAIN, S.A,EATRIS,VHIR,PENUMBRA EUROPE GMBH,ERASMUS MC,Nicolab,Philips (Netherlands),ALLM EMEA GMBH,TRIANECT BV,NORA,Philips (France),CERN,NACAR ESTUDIO SL,SIEMENS HEALTHINEERS AG,UKE,PHILIPS MEDICAL SYSTEMS NEDERLANDFunder: European Commission Project Code: 101172825Overall Budget: 22,955,900 EURFunder Contribution: 14,791,700 EURUMBRELLA is a holistic approach to progress, reshape, and benchmark the overall stroke care pathway and set new and improved standards of care in terms of primary and secondary prevention, rapid access to treatments, early accurate diagnosis, stratification, management and real-time monitoring, therapeutic targets identification, and rehabilitation, recurrent stroke and related cardiovascular events. This innovative approach will transform healthcare systems by improving and harmonizing professionals' workflows in a more patient-centred, digitalized, and communicative manner. UMBRELLA aims to revolutionize stroke management by implementing a comprehensive approach that addresses gaps along the whole continuum of the stroke care pathway. The key paradigm in the project is the multicentric, synergistic "umbrella" strategy for local data collection, harmonization, and standardization along the entire pre-, in-, and post-hospitalization pathway. By establishing specific common data models (CMDs) implemented in each of the 7 top-tier European clinical centres, UMBRELLA will create a federated data platform (U-platform) where Real World Data (RWD)-based AI algorithms can be locally created and validated, to advance personalised diagnosis, risk prediction, and treatment decisions in the acute and post-acute phases of stroke. The algorithms will be then trained in a decentralized manner through a federated learning infrastructure (FL-platform), which preserve data security and privacy, avoiding data centralization or exchange across centres but fostering collective AI-models training. On the other hand, standardized stroke management protocols and procedures will be created and implemented across the participating centres, including the validated usage of advanced digital technologies as solutions to facilitate data collection, visualization, patient engagement, monitoring, outcomes integration, and decision-making across the whole stroke pathway.
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