TY - GEN
T1 - IHELP
T2 - 26th IEEE Symposium on Computers and Communications, ISCC 2021
AU - Manias, George
AU - Op Den Akker, Harm
AU - Azqueta, Ainhoa
AU - Burgos, Diego
AU - Capocchiano, Nikola Dino
AU - Crespo, Borja Llobell
AU - Dalianis, Athanasios
AU - Damiani, Andrea
AU - Filipov, Krasimir
AU - Giotis, Giorgos
AU - Kalogerini, Maritini
AU - Kostadinov, Rostislav
AU - Kranas, Pavlos
AU - Kyriazis, Dimosthenis
AU - Lophatananon, Artitaya
AU - Malwade, Shwetambara
AU - Marinos, George
AU - Melillo, Fabio
AU - Mas, Vicent Moncho
AU - Muir, Kenneth
AU - Nieroda, Marzena
AU - De Nigro, Antonio
AU - Pandolfo, Claudia
AU - Patino-Martinez, Marta
AU - Picioroaga, Florin
AU - Pnevmatikakis, Aristodemos
AU - Syed-Abdul, Shabbir
AU - Tomson, Tanja
AU - Vicheva, Dilyana
AU - Wajid, Usman
N1 - Funding Information:
ACKNOWLEDGMENT The research leading to the results presented in this paper has received funding from the European Union's funded Project iHELP under grant agreement no 101017441.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Artificial Intelligence
KW - Holistic Health Records (HHRs)
KW - Pancreatic Cancer
UR - http://www.scopus.com/inward/record.url?scp=85123199966&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123199966&partnerID=8YFLogxK
U2 - 10.1109/ISCC53001.2021.9631475
DO - 10.1109/ISCC53001.2021.9631475
M3 - Conference contribution
AN - SCOPUS:85123199966
T3 - Proceedings - IEEE Symposium on Computers and Communications
BT - 26th IEEE Symposium on Computers and Communications, ISCC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 September 2021 through 8 September 2021
ER -
