Telemonitoring system for infectious disease prediction in elderly people based on a novel microservice architecture
AuthorsCastillo Sequera, José Luis; Calderón Gómez, Huriviades; Mendoza Pittí, Luis; Vargas Lombardo, Miguel; Gómez Pulido, José Manuel; [et al.]
IdentifiersPermanent link (URI): http://hdl.handle.net/10017/43609
AffiliationUniversidad de Alcalá. Departamento de Ciencias de la Computación; Universidad de Alcalá. Departamento de Medicina y Especialidades Médicas
Calderón-Gómez, H. et al., 2020, "Telemonitoring system for infectious disease prediction in elderly people based on a novel microservice architecture", IEEE Access, vol. 8, pp. 118340-118354
info:eu-repo/grantAgreement/EC/FP7-INCO/ELAC2015%T09-0819/EU/Design and implementation of a low-cost smart system for pre-diagnosis and telecare of infectious diseases in elderly people/SPIDEP
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
This article describes the design, development and implementation of a set of microservices based on an architecture that enables detection and assisted clinical diagnosis within the field of infectious diseases of elderly patients, via a telemonitoring system. The proposed system is designed to continuously update a medical database fed with vital signs from biosensor kits applied by nurses to elderly people on a daily basis. The database is hosted in the cloud and is managed by a flexible microservices software architecture. The computational paradigms of the edge and the cloud were used in the implementation of a hybrid cloud architecture in order to support versatile high-performance applications under the microservices pattern for the pre-diagnosis of infectious diseases in elderly patients. The results of an analysis of the usability of the equipment, the performance of the architecture and the service concept show that the proposed e-health system is feasible and innovative. The system components are also selected to give a cost-effective implementation for people living in disadvantaged areas. The proposed e-health system is also suitable for distributed computing, big data and NoSQL structures, thus allowing the immediate application of machine learning and AI algorithms to discover knowledge patterns from the overall population.