Digital Twins for Personalised Treatment and Monitoring I
This deliverable presents the COMFORTage project’s first comprehensive account of developing and deploying Patient Digital Twins to support personalised treatment and monitoring for dementia and frailty. By combining real-time clinical, sensor, genetic, and lifestyle data, the PDTs act as dynamic, individualised virtual models that enable healthcare professionals to simulate patient trajectories, anticipate risks, and deliver tailored interventions. The document details the system’s layered architecture, encompassing data integration, advanced modelling and simulation, explainable AI-driven recommendations, and intuitive clinician dashboards. It illustrates the system's integration within the broader COMFORTage platform, including the Clinical Decision Support System and opt-in digital health tools. The approach is validated across thirteen European pilot sites, ensuring robustness, scalability, and practical relevance.Beyond the technical foundation, the deliverable emphasises strong ethical, legal, and regulatory compliance, alignment with emerging European standards (GAIA-X, European Health Data Space (EHDS)), and stakeholder involvement from design through deployment. The result is a transformative, user-centred digital health solution that supports proactive, personalised care for ageing populations, establishing COMFORTage as a leader in the application of digital twins for real-world healthcare improvement.This deliverable is the first version of a series of deliverables entitled “Digital Twins for Personalised Treatment and Monitoring” that seek to encapsulate and describe the work conducted in the context of T4.3 – “Digital Twins for Personalised Treatment and Monitoring”. The second and last version of this series will be provided on M36 of the project.
Authors
- Stylianos Kokkas (ORCID: https://orcid.org/0000-0002-1639-0635)
- Constantinos Stylianou (ORCID: https://orcid.org/0000-0001-5692-370X)
- Fátima González Palau
- George Manias
- Alexander Scherrer
- Spiros Kleftakis
- Ioannis Dontas
Institutions
- Tilburg University (NL)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-11-28
- DOI
- https://doi.org/10.5281/zenodo.18887260
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00