LifeChamps

LifeChamps provides an advanced analytics framework for Quality of Life support to cancer patients during and after their treatments. This framework aims to provide timely, accurate and multi-dimensional clinical decision support at the point of care. It will employ advanced AI techniques and a combination of cloud and edge-based processing.

Call:H2020-SC1-DTH-2018-2020
 

Budget: 4.999.915,00 EUR
 

Project ID:875329

Type of action:RIA
 

Newsletter Vol.2

We are pleased to announce the publication of the second issue of the LifeChamps newsletter.

 

PDF Link: Vol.2

Kick off meeting

The LifeChamps Kick-off Meeting took place on January 20th & 21st, 2020, at the Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece. AUTH, as the Project Coordinator, successfully hosted and organized the meeting, setting the basis for a fruitful collaboration. The 15 project partners, coming from nine countries, participated via their representatives, who contributed to the meeting with interesting and informative presentations.

During the two days of the meeting, the LifeChamps consortium had the opportunity to discuss further project’s objectives, set action points on specific work packages and exchange knowhow, experiences and ideas for the coming period.

Newsletter Vol.1

We are pleased to announce the publication of the first issue of the LifeChamps newsletter.

 

PDF Link: Vol.1

Actions and Results

The LifeChamps project targets recovering older (pre-frail and frail) cancer patients, but also caregivers and multidisciplinary health professionals with a comprehensive solution capable of offering tools and mechanisms to promote patients’ empowerment and improved quality of life via timely and more accurate clinical decision support at the point of care. As such, we propose a structured solution based on a scalable predictive engine in which we determine which features affect the oncological patient’s quality of life the most, during and after the treatment. These results will then be used to feed a recommender system, offering personalized healthcare services such as symptom monitoring, treatment and rehabilitation. A multi-factorial frailty model will allow to stratify sub-clinical frail groups of geriatric cancer patients towards more personalized treatment.

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