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