Opportunities and Challenges for Research on Intelligent Algorithms for Phealth

Abstract

pHealth systems, usually defined as personal, pervasive, precision, preventive or predictive health systems, are a valuable concept, enabling the management of the disease by the patient himself, contributing towards the reduction of healthcare costs while improving the health quality. Among others, these solutions make possible the remote monitoring using non-intrusive sensors, to seamless access multiple sources of data, including physiological signals, providing professionals with a global and reliable view of the patient's status. While these new advances have created new opportunities, at the same time new challenges have been introduced. Of particular importance are appropriate data analysis algorithms and tools, which are decisive to complement telemonitoring technologies. In effect, these are fundamental in order to fully accomplish the real potential of the collected data and, therefore, to generate meaningful information from it. Data analysis methods, including machine learning and data mining techniques, as well as algorithms and models for physiological signal modelling and processing, are examples of such useful tools. One of the main objectives of Project LINK, an EU-H2020 funded project, is to deepen the cooperation between three institutions in the area of intelligent processing applied to the management of cardiovascular diseases in pHealth context. One of the main activities is to identify new research opportunities, by promoting the integration and exploiting synergies among the different research groups of the consortium, supporting the research activities to be pursued along the duration of the project as well as working towards sustainability beyond LINK. These research questions will result from the intersection of the inputs provided by Innovation and research forum and clinical partners, key competences inside the consortium and shared research interests. An initial analysis, conducted inside the consortium, enabled to identify some of thes