Network for Informational Methods in Supporting Persons Predisposed to preventable Strokes using common devices (NIMSPPS)
The aim of the NIMSPPS proposal is to develop an innovative R&D network to support people predisposed to preventable strokes. The network will give computer scientists, doctors and healthcare professionals the opportunity to better understand stroke-related mechanisms, the impact of risk factors, recognize the predictability and avoidability of each factor, and also exchange information to find best practices in stroke prevention and jointly develop and use new technologies, methods and algorithms for the early detection of stroke risk factors. Thus, the NIMSPPS network allows scientists involved to cover the entire stroke development pathway.
The number of people affected by strokes in the Danube region, as well as throughout Europe, is growing steadily. This points to a looming problem in Europe because the economic burden of caring for the people affected by strokes within the European Union alone is estimated at 38 billion euros per year. The number of stroke patients is expected to increase from 1.1 million in 2000 to 1.5 million in 2025.
High financial expenditure, psychological and social disabilities, social rejection - all of these issues underscore the importance of addressing the health risk of strokes in the society. So far, certain important issues, such as features of the clinical polymorphisms, the disease-causing mechanisms, the risk factors, the precise and acute treatment, and the prophylaxis are still not sufficiently resolved. Many people are unaware that strokes can be avoided and that survivors can also live a normal life. Thus, prevention and avoidance are the best approaches to reducing the social burden of strokes.
It is pertinent to develop computer-based methods and models on the basis of controllable, and therefore, avoidable risk factors, so as to support people predisposed to preventable stroke. The project aims to assess the main preventable risk factors for people aged 35-65 and to develop computer-based methods and models that propose measures to maintain the overall health of the individuals concerned, or even to improve the health of diseased people.