Authors:
Ourania Manta
1
;
5
;
Nikolaos Vasileiou
1
;
Olympia Giannakopoulou
1
;
Konstantinos Bromis
1
;
Ioannis Kouris
1
;
Maria Haritou
1
;
Lefteris Koumakis
2
;
George Spanoudakis
2
;
Irina Nicolae
3
;
C. Nechifor
3
;
Miltiadis Kokkonidis
4
;
Michalis Vakalelis
4
;
Yorgos Goletsis
5
;
Maria Roumpi
5
;
Heraklis Galanis
6
;
Panagiotis Dimitrakopoulos
6
;
George Matsopoulos
1
and
Dimitrios D. Koutsouris
1
Affiliations:
1
Biomedical Engineering Laboratory, Institute of Communication and Computer Systems, National Technical University of Athens, 15773 Athens, Greece
;
2
Sphynx Technology Solutions AG, 6300 Zug, Switzerland
;
3
Configuration Technologies, Data Analytics and Artificial Intelligence, Siemens Technology, 500097, Brașov, Romania
;
4
AEGIS IT Research GmbH, 38106 Braunschweig, Germany
;
5
Biomedical Research Institute, FORTH, University of Ioannina, Ioannina, Greece
;
6
Datamed SA, Athens, 15124, Greece
Keyword(s):
Clinical Site Backend, Data Analysis, Global Insights Cloud, Heart Failure, Integration, Machine Learning, Patient Edge, Personalised Interventions, Retention Platform, Testing.
Abstract:
This paper introduces the RETENTION Platform, an integrated healthcare data management system meticulously crafted to support personalised interventions, thereby enhancing outcomes for heart failure (HF) patients. Comprising three fundamental components—the Global Insights Cloud (GIC), the Clinical Site Backend (CSB), and Patient Edge (PE)—the platform coordinates a sophisticated array of functions. The GIC facilitates data analysis and machine learning model training, while the CSB enables daily patient check-ups, data gathering, and intervention application. The Patient Edge enables continuous monitoring and feedback collection from patients. The system is deployed using virtual machines (VMs) and Docker containers on a cloud-based infrastructure. Integration and testing procedures are outlined to safeguard system functionality. This paper provides a comprehensive overview of the RETENTION Platform’s architecture and highlights its potential for improving healthcare delivery throug
h personalised interventions.
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