What is Capable Project?
The overall aim of CAPABLE is to combine the most advanced technologies for data and knowledge management with a sound socio-psychological approach in order to develop a coaching system for improving the quality of life of cancer home patients. The system aims at early detecting and managing cancer-related issues and at satisfying the needs of patients and their home caregivers.
Demo of the final version of the CAPABLE system is now available. Please see the Demo page for more details.
CAPABLE project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875052.
Latest News
Final Presentation of PhysicAI Garden
Today, during the 3rd and final periodic review, the CAPABLE artists in residence – Anna Dumitriu and Alex May – provided the final presentation of
Success Story Interview with Prof. Quaglini
Professor Silvana Quaglini was interviewed by the Health-NCP-Net 3.0 as part of the Impact Success Stories series where the coordinators of outstanding H2020 projects talk
Project Formally Finished
On January 31, 2024, we had the last internal project meeting. As a nice farewell gift, we received a touching note from one of the
OVERALL GOAL
To develop a support system for improving the quality of life of cancer home patients by combining technologies for data and knowledge management with socio-psychological models and theories
MAIN OBJECTIVES
User Experience
- Identifying new patients’ and home caregivers’ needs
- Promoting patients’ engagement
- Improving patients’ compliance to treatment
- Facing patients’ emotional issues
- Supporting the use of computer-interpretable guidelines (CIGs) for healthcare professionals
- Improving clinical workflows
Technological
- Promoting data management through standard terminologies, data modeling, and FAIR principles
- Integrating (big) data coming from different sources
- Integrating data, knowledge and AI
- Using Health Technology Assessment as the basis for system development
Scientific
- Identifying gaps between clinical guidelines and patient needs
- Identifying new adverse effects
- Developing new models of disease course
- Managing Multimorbidity through CIG-based decision support
- Extending guideline formalization languages to include predictive models