Patients with Parkinson, Multiple Sclerosis and Stroke
Patients with Parkinson, Multiple Sclerosis and Stroke (PMSS) will benefit from the individual personalized care based on AI decision making models that may be tuned to cover various aspects of PMSS brain diseases and a variety of medical care models, as well as methods to calibrate such models for individuals.
Advanced data analytics and AI systems will be deployed in order to:
-
1. continuously monitor their health status and overall cognitive capacity.
-
2. evaluate outcomes that matter to patients, such as fatigue, psychosocial status, anxiety and depression, quality of life, and satisfaction with the technology and Tele-healthcare.
Healthcare providers and professionals
The developed AI and Big Data methods will be able to integrate and handle efficiently heterogeneous datasets and datasets with missing values (incomplete). Different patients and outcomes will be monitored and recorded at different frequencies mainly due to the differences in their health status and the symptoms, enabling clinicians to design personalised monitoring plans.
Relapses have a major influence on clinicians’ treatment decisions for patients with PMMS, therefore equipping clinicians with advanced tools for on-time prediction of relapse is of outmost importance to identify the most appropriate treatment, ensuring effective care for these patients over time. Monitoring motor function and sleep characteristics has the potential to predict the course of the disease, in particular the prediction of relapse or worsening that is fundamental for improving drugs and rehabilitative treatments efficacy, resulting in a better care and quality of life for the people affected by PMMS.
Overall, ALAMEDA will benefit healthcare professionals and providers by broadening the current panorama of diagnostic and monitoring tools available for clinical and healthcare practice.
Formal and informal caregivers
ALAMEDA will provide a comprehensive, multi-sensor monitoring solution for individuals with brain disorders, deploying a great variety of sensors to monitor their physiological status, overall health and lifestyle aspects. The heterogeneous sensor data will be integrated in an intelligent manner, resulting in a comprehensive picture of the person’s current status and its evolution over time, allowing the healthcare professionals to determine the best care approach in each case. Each sensing modality will be analyzed separately, and their results will be integrated in a semantically meaningful manner, in line with user requirements dictated by healthcare professionals, the informal caregivers, as well as the patients themselves.
Their daily activities in terms of motor function will be monitored by wearables, while both they and their caregivers will provide input about their fluctuating condition; input from caregivers will be especially important in cases in which the patients themselves do not have a complete realization of their condition, for example during their cognitive fluctuations, or when they manifest dyskinesias, of which they may not be aware.