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dAIbetes

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Federated virtual twins for privacy preserving personalised outcome prediction o...

Federated virtual twins for privacy preserving personalised outcome prediction of type 2 diabetes treatment Virtual twins may be used as prognostic tools in precision medicine for personalised disease management. However, their training is a data hungry endeavour requiring big data to be integrated across diverse sources, which in turn... ver más

31/12/2028

Líder desconocido

9M€

Presupuesto del proyecto: 9M€

Líder del proyecto Líder desconocido

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-11-14

HORIZON-HLTH-2023-TOOL-05-03: Integrated, multi-scale computation... Expected Outcome:This topic aims at supporting activities that are enabling or contributing to one o...

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Duración del proyecto: 61 meses Fecha Inicio: 2023-11-14
Fecha Fin: 2028-12-31

Líder del proyecto Líder desconocido

Presupuesto del proyecto 9M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Virtual twins may be used as prognostic tools in precision medicine for personalised disease management. However, their training is a data hungry endeavour requiring big data to be integrated across diverse sources, which in turn is hampered by privacy legislation such as the General Data Protection Regulation. Privacy-enhancing computational techniques, like federated learning, have recently emerged and hold the promise of enabling the effective use of big data while safeguarding sensitive patient information. In dAIbetes, we build on this technology to develop a federated health data platform for clinical application of the first internationally trained federated virtual twin models. Our primary medical objective is personalised prediction of treatment outcomes in type 2 diabetes, which afflicts 1 in 10 adults worldwide and causes annual expenditures of ca. 893 billion EUR. While healthcare providers are becoming increasingly effective at targeting diabetes risk factors (e.g. diet or exercises), no guidelines as to the expected outcome for a given treatment for a specific patient exist. To address this urgent, yet unmet need, the federated dAIbetes technology will harmonise existing data of ca. 800,000 type 2 diabetes patients of 4 cohorts distributed across the globe, and learn prognostic virtual twin models. Those will be validated for their clinical relevance and applied in clinical practice through a dedicated software. We aim to demonstrate that our personalised predictions have a prediction error that is at least 10% lower than that of population average-based models. This federated virtual twin technology will enable personalised disease management and act as a blueprint for other complex diseases. Our consortium combines expertise in artificial intelligence, software development, privacy protection, cyber security, and diabetes and its treatment. Ultimately, we aim to resolve the antagonism of privacy and big data in cross-national diabetes research.

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