Bio-Data Analysis

Biomedical data sources and types: wireless sensor networks and smart devices (behavioural lifestyle data, biochemical parameters), electronic health record (demographical, medical imaging, laboratory exams, treatment), high throughput –omics technologies (genetic and molecular diagnostic data). Individualized behavioural, clinical and biological profiling. Generation of new clinical knoweledge shifting the focus from reactive to preventive, predictive, personalized and participatory healthcare delivery. Advanced data analytics based on statistics, machine learning, ensemble learning and deep learning towards the: (i) identification/extraction of new biomarkers, (ii) development of adaptive predictive models of an individual’s health evolution and (iii) the provision of highly personalized healthcare treatment schemes. Specific issues concerning the nature of the biomedical data (e.g. unbalanced distribution, missing data). Multi-layer and multi-scale analysis for the enhanced integration of a multitude of heterogeneous biomedical data. Patient empowerment and disease management systems. Clinical Decision Support Systems. Applications to metabolic diseases, cardiovascular diseases and cancer.