Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

dAIbetes

Financiado

Cerrado

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

UHAM

9M€

Presupuesto del proyecto: 9M€

Líder del proyecto

UNIVERSITY OF HAMBURG No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-01-01

HORIZON-HLTH-2023-TOOL-05-08: Pandemic preparedness and response:... Expected Outcome:This topic aims at supporting activities that are enabling or contributing to one o...

I+D +1

Cerrada hace 1 año

0% 100%

Información adicional privada

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

Participantes

UHAM

Lider

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Proyectos interesantes

RTC-2017-6038-1 HERRAMIENTA PARA EL DESCUBRIMIENTO DE NUEVAS VARIABLES IMPLI... 356K€ Cerrado

BETTER Better rEal-world healTh-daTa distributEd analytics Research... 10M€ Cerrado

PHASE IV AI Privacy compliant health data as a service for AI developmen... 7M€ Cerrado

FLUTE Federate Learning and mUlti-party computation Techniques for... 6M€ Cerrado

DataTools4Heart A European Health Data Toolbox for Enhancing Cardiology Data... 8M€ Cerrado

PGC2018-102145-B-C22 EVOLVING TOWARDS DIGITAL TWINS IN HEALTHCARE 12K€ Cerrado

Duración del proyecto: 59 meses Fecha Inicio: 2024-01-01
Fecha Fin: 2028-12-31

Líder del proyecto

UNIVERSITY OF HAMBURG No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

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.

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores