iHELP: Personalised Health Monitoring and Decision Support Based on Artificial Intelligence and Holistic Health Records
George Manias, Harm op den Akker, Ainhoa Azqueta-Alzúaz, Diego F. B. Sarzosa, Nikola Dino Capocchiano, Borja Llobell Crespo, Athanasios Dalianis, Andrea Damiani, Krasimir Filipov, Giorgos Giotis, Maritini Kalogerini, Rostislav Kostadinov, Pavlos Kranas, Dimosthenis Kyriazis, Artitaya Lophatananon, Shwetambara Malwade, George Marinos, Fabio Melillo, Vicent Moncho Mas, Kenneth Muir, Marzena Nieroda, Antonio De Nigro, Claudia Pandolfo, Marta Patiño-Martı́nez, Florin Picioroaga, Aristodemos Pnevmatikakis, Shabbir Syed-Abdul, Tanja Tomson, Dilyana Vicheva, Usman Wajid
Abstract
Scientific and clinical research have advanced the ability of healthcare professionals to more precisely define diseases and classify patients into different groups based on their likelihood of responding to a given treatment, and on their future risks. However, a significant gap remains between the delivery of stratified healthcare and personalization. The latter implies solutions that seek to treat each citizen as a truly unique individual, as opposed to a member of a group with whom they share common risks or health-related characteristics. Personalisation also implies an approach that takes into account personal characteristics and conditions of individuals. This paper investigates how these desirable attributes can be developed and introduces a holistic environment, the iHELP, that incorporates big data management and Artificial Intelligence (AI) approaches to enable the realization of data-driven pathways where awareness, care and decision support is provided based on person-centric early risk prediction, prevention and intervention measures.