New Collaboration with Artists

The CAPABLE project is delighted to welcome artists Anna Dumitriu and Alex May to the team. The artists will deeply engage with CAPABLE research to create a new body of artworks. They will bring their extensive track records of working in collaboration with scientists to the project.

Virtual Meeting

The CAPABLE project team is meeting again on June 15-17, 2020. Due to the pandemic, the meeting will be held virtually. Wish us a frutiful and productive time.

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New Collaboration with Artists

The CAPABLE project is delighted to welcome artists Anna Dumitriu and Alex May to the team. The artists will deeply engage with CAPABLE research to create a new body of artworks.

Virtual Meeting

The CAPABLE project team is meeting again on June 15-17, 2020. Due to the pandemic, the meeting will be held virtually. Wish us a frutiful and productive time.

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next arrow
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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. 

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
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