📋 Tender Overview
Federated Virtual Twins For Privacy-Preserving Personalised Outcome Prediction Of Type 2 Diabetes Treatment
Twins To Predict Diabetes Treatment Outcomestype 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.
Funded Under
Health
Total Cost
€ 8 938 145,00
Eu Contribution
€ 8 938 145,00
Coordinated By
Universitaet Hamburg
Germany
Start Date
1 January 2024
End Date
31 December 2028
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 diabe
