New EU collaborative project aims to improve prevention and treatment of stroke

"Charité - Universitätsmedizin Berlin is hosting a new EU collaborative project. Funded via the European 'Horizon 2020 Framework Program for Research and Innovation', the project aims to improve both the prevention and treatment of stroke, and the patients' quality of life after stroke. It is hoped that computer-based predictive modeling will soon provide a means of personalizing and optimizing treatment strategies. The project, which is set to start in May, will be led by Dr. Dietmar Frey of Charité's Department of Neurosurgery. Stroke is a major problem, and its impact is not limited to patients and their families. Aside from wide-ranging public health implications, stroke also has a considerable public health and socioeconomic impact. The aim of the PRECISE4Q project is to minimize the burden of stroke, both for the individual and for society. The project will collect large data sets from different sources, integrating them into self-learning computer models. The predictive computer models will allow to develop personalized prevention and treatment strategies for patients with stroke. Both the computer models and resulting treatment recommendations will be based on the needs of the individual patient. Rather than being limited to acute stroke treatment, these computer models will also address other treatment phases such as prevention, rehabilitation and reintegration. In addition to personalized coping strategies, they will also focus on supporting the patient's well-being and their reintegration into social and work life. Using information obtained from a range of different sources, the researchers will collect clinical, physiological, genetic and biochemical information as well as medical imaging data. These data will undergo standardization and analysis for further use as structured data and will be used to design, train and test computer models. The generated predictive models are based on the concepts of artificial intelligence and, in particular, machine learning. The predictive capability and clinical precision will be validated with real clinical data generated by clinical studies and analyses of big data sets: health registries, cohort studies, health insurance data, electronic health records. The models will eventually be integrated into a comprehensive digital information platform, the 'Digital Stroke Patient Platform'. Intended as a clinical decision support system, this platform will help guide clinicians through the clinical decision-making process. Another aim of this project is the development of 'EUROPE-Stroke', an open research platform that will support the aggregation, integration and analysis of data as well as promoting the principles of 'Open Science' by supporting the dissemination of reproducible research results. "