Medical Prognosis of Infectious Diseases in Nursing Homes by Applying Machine Learning on Clinical Data Collected in Cloud Microservices
AuthorsGarcés Jiménez, Alberto; Calderón Gómez, Huriviades; Gómez Pulido, José Manuel; Gómez Pulido, Juan Antonio; Vargas Lombardo, Miguel; [et al.]
IdentifiersPermanent link (URI): http://hdl.handle.net/10017/50893
AffiliationUniversidad de Alcalá. Departamento de Ciencias de la Computación; Universidad de Alcalá. Departamento de Enfermería y Fisioterapia; Universidad de Alcalá. Departamento de Medicina y Especialidades Médicas
Garcés Jiménez, A. et al. 2021, "Medical Prognosis of Infectious Diseases in Nursing Homes by Applying Machine Learning on Clinical Data Collected in Cloud Microservices", International Journal of Environmental Research and Public Health, vol. 18, no. 24, art. no. 13278.
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JCR Web of Science; Year: 2020 in Categories: Public, Environmental & Occupational Health: Q1, Current Impact Factor: 3.390, 5-year Impact Factor: 3.789.
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-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Background: treating infectious diseases in elderly individuals is difficult; patient referral to emergency services often occurs, since the elderly tend to arrive at consultations with advanced, serious symptoms. Aim: it was hypothesized that anticipating an infectious disease diagnosis by a few days could significantly improve a patient?s well-being and reduce the burden on emergency health system services. Methods: vital signs from residents were taken daily and transferred to a database in the cloud. Classifiers were used to recognize patterns in the spatial domain process of the collected data. Doctors reported their diagnoses when any disease presented. A flexible microservice architecture provided access and functionality to the system. Results: combining two different domains, health and technology, is not easy, but the results are encouraging. The classifiers reported good results; the system has been well accepted by medical personnel and is proving to be cost-effective and a good solution to service disadvantaged areas. In this context, this research found the importance of certain clinical variables in the identification of infectious diseases. Conclusions: this work explores how to apply mobile communications, cloud services, and machine learning technology, in order to provide efficient tools for medical staff in nursing homes. The scalable architecture can be extended to big data applications that may extract valuable knowledge patterns for medical research.
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