Detailed description of project

The project is focused on improving the understanding and management of late effects in adolescent and young adult (AYA) cancer survivors. The key objectives are:
● Developing a digital phenotyping platform that integrates big data and AI for personalized patient monitoring.
● Creating a multi-component psychosocial intervention planner to assist patients in coping with long-term cancer effects.
● Implementing a multi-stakeholder adaptive follow-up tool that involves healthcare providers, caregivers, and social services to ensure continued post-treatment support.
● Establishing a living lab environment where innovative care solutions can be tested and validated.
The project also aligns with European Health Data Space (EHDS) recommendations, ensuring data security and interoperability for large-scale healthcare studies.

Type and scope of work provided

1. The project involves several work packages (WPs), each addressing a specific area of research and technological development
2. Coordination and Management (WP1)
3. Ensuring effective project administration, compliance with funding regulations, and risk assessment.
4. Digital Phenotyping and Data Analytics (WP3)
5. Developing AI-driven models to analyze patient data from multiple sources (clinical records, wearables, lifestyle tracking)
6. Multi-Component Psychosocial Intervention Planner (WP4)
7. Designing customized psychological interventions for AYA cancer survivors using digital tools.
8. Implementing a conversational AI chatbot for patient interaction and mental health support
9. Adaptive Follow-Up System (WP5)
10. Creating a multi-stakeholder dashboard to facilitate communication between doctors, social workers, and caregivers.
11. Enabling real-time remote monitoring of patient well-being.
12. Big Data and AI for Late Effects Prediction (WP7)
13. Using machine learning models to predict late-onset complications in cancer survivors.
14. Integrating biometric, genetic, and behavioral data for precision medicine applications
15. Clinical Study and Validation (WP8)
16. Conducting large-scale clinical trials across multiple European healthcare institutions.
17. Evaluating the effectiveness of AI-driven interventions in real-world settings.
18. Impact Assessment and Dissemination (WP9 & WP10)
19. Measuring the societal and economic impact of the developed solutions.
20. Disseminating findings through scientific publications, conferences, and policy recommendations

This project represents a major step forward in AI-powered personalized medicine, improving the quality of life for cancer survivors through data-driven healthcare solutions.