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Federated virtual twins for privacy-preserving personalised outcome prediction of type 2 diabetes treatment

Project description

Twins to predict diabetes treatment outcomes

Type 2 diabetes, affecting 1 in 10 adults globally, presents a significant healthcare challenge with varying treatment outcomes. Despite advancements in targeting risk factors, predicting treatment effectiveness for individual patients remains elusive. This gap underscores the need for personalised medicine in diabetes management. In this context, the EU-funded dAIbetes project will build virtual twin models. Trained on diverse datasets while preserving patient privacy, these models aim to predict treatment outcomes with unprecedented accuracy. By integrating data from 800 000 patients worldwide, dAIbetes aims to personalise disease management and revolutionise clinical practice. Overall, it aims to reduce prediction errors by 10 % compared to population-based models.

Objective

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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(opens in new window) HORIZON-HLTH-2023-TOOL-05

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Coordinator

UNIVERSITY OF HAMBURG
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 2 054 775,00
Address
MITTELWEG 177
20148 Hamburg
Germany

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Region
Hamburg Hamburg Hamburg
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 2 054 775,00

Participants (11)

Partners (1)

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