A REAL WORLD DATA-DRIVEN APPROACH
Innovative model driven big data analytics, statistical, artificial intelligence and machine learning techniques are deployed to offer user friendly verifiable decision making capabilities regarding interventions and their effectiveness by generating new insights into disease patterns to continuously refine its patient management capabilities.
CLINICAL SITE BACK-END
The different types of data that RETENTION will collect and analyse include: Standard demographic data (e.g., age, sex, marital status, home address, socio-economic status).
Medical history data.
Data from baseline clinical testing relevant to the particular conditions and patients targeted by the project (e.g., N-terminal pro-B-type natriuretic peptide) and periodic patient clinical testing (electrocardiography/ECG, blood tests, cardio-pulmonary exercise testing).
Subjective patient assessments (depression and quality of life questionnaires).
Daily collected data including patient’s weight body fat, skeletal muscle and body water, blood pressure (excluding patients with VAD), heart rate, peripheral capillary oxygen saturation, deep and light sleep phases and sleep interruptions, fluid balance (intake/diuresis), adherence to prescribed medication, nutrition intake, circadian rhythm, physical activity data (steps, distance, floors climbed, calories burned).
Daily collected data from VADs, if used by the patient.
Data will be complemented and correlated with Real World Data (RWD) derived from non-medical sources including:
Patient living space data (ambient light, temperature and humidity, indoor locations).
Patient living environment data (extreme temperatures and weather conditions, pollutants obtained from Copernicus services).
Patient outdoor movement and location data and sunlight exposure.
This RETENTION-Project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 965343.
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