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.
CAPABLE project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875052.
Latest News
6th Consortium Meeting in Amsterdam
Next Consortium Meeting is starting! The 6th CM will be held in a hybrid mode in Amsterdam, Netherlands. Specifically, it will be hosted by the
Bitsens shortlisted by the EC Innovation Radar
Today we are excited to inform you that one member of the CAPABLE Consortium, namely Bitsens UAB, is selected among top European innovators and shortlisted
PhysicAI Garden video updated
The video demonstrating the PhysicAI Garden (the project’s artwork) has been updated with music, thus offering an extra experience. Moreover, an Italian version has been
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