Telemonitoring system for infectious disease prediction in elderly people based on a novel microservice architecture
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 con...
| Autores: | , , , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Recursos: | Universidad de Alcalá (UAH) |
| Repositorio: | e_Buah Biblioteca Digital Universidad de Alcalá |
| Idioma: | inglés |
| OAI Identifier: | oai:ebuah.uah.es:10017/43609 |
| Acesso em linha: | http://hdl.handle.net/10017/43609 https://dx.doi.org/10.1109/ACCESS.2020.3005638 |
| Access Level: | acceso abierto |
| Palavra-chave: | Artificial intelligence e-Health Elderly people Infectious diseases Microservice architecture Microservices Telemonitoring Informática Medicina Computer science Medicine |
| Resumo: | 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. |
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