Project code: NET-2016-02361805
This project aims to develop and implement a multicomponent intervention platform of common strategies to improve tailored management of community-dwelling for older people with multimorbidity and polypharmacy. In particular, our contribution lies in the definition of a care transition model in a home-oriented protected and sensorized area where advanced technologies based on Computer Vision and Artificial Intelligence are used to monitor clinical, functional and mobility parameters.
Project code: FISM 2018/R/5
This project investigates the role of brain network connectivity and machine learning for predicting disease worsening and cognitive impairment in patients with multiple sclerosis. Multiple sclerosis (MS) is a chronic condition showing a high degree of individual variability in clinical symptoms, disease course and rates of accumulation of disability. Because of the complexity of MS disease, in which damage of several regions contributes to clinical impairment, graph theory ( modelling the brain as a network or a “graph”) is likely to provide tools useful to represent and analyze the structural disconnection on one hand, and the functional compensation on the other. The main objective of the project is to combine the application of advanced computational methods to structural and functional network connectivity, with machine learning techniques in order to predict clinical worsening in our patients’ cohort.