Medical Care Evolution
MEDOLUTION (Medical care evolution) is an European project funded by France in the context of the call 1 of the ITEA 3 Eureka program for the period: 2015-2019. The consortium is composed of 26 partners from 5 countries (France, Germany, Netherlands, Canada, Turkey). The MEDOLUTION project’s vision is providing new technologies and methodology with an architecture and a set of best practices for building ICT solutions that can bring on the long-term reductions in the cost of healthcare along with an improvement in the quality of life of patients. The project aims to emphasis on artificial intelligence to develop long-term monitoring and real-time decision support system that can operate in smart environments based on the Internet of things. The latter integrate professional and user-created data. This leads to relevant information to support patients and healthcare professionals in their decision making on diagnosis, treatment and further monitoring; from reactive to preventive. Medolution builds upon the results of Medusa that provides collaborative cloud access to medical information relevant in critical situations.
Medolution provides a collaborative cloud platform with powerful AI systems that can deliver medical information and decision support services at the right time, at the right place, in the most effective, intelligent and costeffective way. These services and information are relevant for long-term follow-up of patients and short-term monitoring. The Medolution platform core integrates control and connectors of heterogeneous devices and provides real-time data analytics based on the latest machine learning methods.
The project will prepare several case studies and demonstrators showing the feasibility in healthcare settings such as LVAD monitoring, stroke early detection and diagnosis before arrival to the hospital, chronic patient follow-up and Parkinson diagnosis and rehabilitation at home.
The LISSI laboratory is contributing in the development of Big data analytics platform connected to ambient intelligent environment. This platform developed by using the BDCF framework can assure non-intrusive monitoring of patients in all settings, and can provide health professionals with decision support, prediction and analysis tools.
The LISSI laboratory is involved in the development and validation of Artificial intelligence-based decision-support tools, which integrates together data-driven (fusion / classification) and knowledge-oriented (semantic) methods. For instance, the LISSI laboratory is developing machine learning algorithms to improve the recognition of patients physical exercises, analyzing patient gait for diagnosing the Parkinson disease, etc. These algorithms are used in the use cases dealing with the longitudinal and contextualized follow-up of chronic patients.
