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SYNTHEMA

Financiado

Cerrado

Synthetic generation of hematological data over federated computing frameworks

Haematological diseases (HDs) are a large group of disorders resulting from quantitative or qualitative abnormalities of blood cells, lymphoid organs and coagulation factors. Despite most of them (~74%) are rare, the overall numbe... ver más

30/11/2026

UPM

7M€

Presupuesto del proyecto: 7M€

Líder del proyecto

UNIVERSIDAD POLITÉCNICA DE MADRID No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 3946

Fecha límite participación Sin fecha límite de participación.

Ver 16 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-11-10

HORIZON-HLTH-2022-IND-13-02: Scaling up multi-party computation,... ExpectedOutcome:This topic aims at supporting activities that are enabling or contributing to one or...

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

16 Participantes

UPM

685.00K€ | Lider

i-HD

410.00K€ | Participante

ASSISTANCE PUBLIQUE HOPITAUX...

311.25K€ | Participante

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Duración del proyecto: 48 meses Fecha Inicio: 2022-11-10
Fecha Fin: 2026-11-30

Líder del proyecto

UNIVERSIDAD POLITÉCNICA DE MADRID No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 3946

Presupuesto del proyecto 7M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Haematological diseases (HDs) are a large group of disorders resulting from quantitative or qualitative abnormalities of blood cells, lymphoid organs and coagulation factors. Despite most of them (~74%) are rare, the overall number of HD affected patients worldwide is important, placing a considerable economic burden on healthcare systems and societies. Despite the existence of several collaborative research groups at national and EU level, current clinical approaches are often ineffective, particularly for rarest conditions, due to the relatively low number of patients per disease and the high number of unconnected clinical entities. SYNTHEMA aims to establish a cross-border data hub where to develop and validate innovative AI-based techniques for clinical data anonymisation and synthetic data generation (SDG), to tackle the scarcity and fragmentation of data and widen the basis for GDPR-compliant research in RHDs. The project will focus on two representative RHD use cases: sickle-cell disease (SCD) and acute myeloid leukaemia (AML). SYNTHEMA will develop a federated learning (FL) infrastructure, equipped with secure multiparty computation (SMPC) and differential privacy (DF) protocols, connecting clinical centres bringing standardised, interoperable multimodal datasets and computing centres from academia and SME. This framework will be utilised to train the developed algorithms and perform SMPC-based global model aggregation in a privacy-preserving fashion. The resulting data will be validated for their clinical value, statistical utility and residual privacy risks. The project will develop legal and ethical frameworks to guarantee privacy by-design in the collection and processing of health-related personal data and attain an ethics-wise algorithm co-creation. Project outcomes, including pipelines, standards and data, will be made openly available to stakeholders in the healthcare, academia and industry field, and contribute to existing rare disease registries

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