carre project full details
Timeline:01 Nov 2013 - 31 Oct 2016
Personalised patient empowerment for cardiorenal comorbidities
The CARRE project investigates information and communication technologies for empowering patients with comorbidities (multiple co-occurring medical conditions), or persons with increased risk of such conditions, especially in the case of chronic cardiac and renal disease patients.
KMi will provide the semantic "glue" linking biometric sensor data, medical ontologies and Linked Data together for patient-focused decision support and medical education.
The CARRE project has received a 3-year EC funding from the European Community 7th Framework Programme FP7-ICT-2013 work programme under grant agreement no. 611140.
- Democritus University of Thrace (Greece)
- University of Bedfordshire (UK)
- Vilnius University Hospital Santarikių Klinikos (Lithuania)
- Kaunas University of Technology (Lithuania)
- Industrial Research Institute for Automation and Measurements (Poland)
15 Mar 2017
28 Oct 2016
15 Sep 2016
20 Jun 2016
08 Sep 2015
Zhao, Y., Parvinzamir, F., Wei, H., Liu, E., Deng, Z., Dong, F., Third, A., Lukoevičius, A., Marozas, V., Kaldoudi, E. and Clapworthy, G. (2016) Visual Analytics for Health Monitoring and Risk Management in CARRE, Workshop: IDAVis 2016: Workshop on intelligent data analytics and visualization at 10th International Conference on E-Learning and Games, Hangzhou, China
Kaldoudi, E., Drosatos, G., Portokallidis, N. and Third, A. (2016) An Ontology based Scheme for Formal Care Plan Meta-Description, XIV Mediterranean Conference on Medical and Biological Engineering and Computing, Paphos, Cyprus
Third, A., Kaldoudi, E., Gkotsis, G., Roumeliotis, S., , K. and Domingue, J. (2015) Capturing Scientific Knowledge on Medical Risk Factors, Workshop: 1st International Workshop on Capturing Scientific Knowledge at 8th International Conference on Knowledge Capture, Palisades, NY, USA
Gkotsis, G., , k., Pedrinaci, C., Domingue, J. and Liakata, M. (2014) It's all in the Content: State of the art Best Answer Prediction based on Discretisation of Shallow Linguistic Features, ACM Web Science 2014 Conference, Indiana University, Bloomington, USA, pp. 9