Medical Prognosis of Infectious Diseases in Nursing Homes by Applying Machine Learning on Clinical Data Collected in Cloud Microservices

Alberto Garcés-Jiménez, Huriviades Calderón-Gómez, José M. Gómez-Pulido, Juan A. Gómez-Pulido, Miguel Vargas-Lombardo, José L. Castillo-Sequera, Miguel Pablo Aguirre, José Sanz-Moreno, María-Luz Polo-Luque and Diego Rodríguez-Puyol
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Alberto Garcés-Jiménez: Foundation for Biomedical Research, Hospital Príncipe de Asturias, 28805 Alcalá de Henares, Spain
Huriviades Calderón-Gómez: Department of Computer Science, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
José M. Gómez-Pulido: Department of Computer Science, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
Juan A. Gómez-Pulido: Department of Technologies of Computers and Communications, Universidad de Extremadura, 10003 Cáceres, Spain
Miguel Vargas-Lombardo: E-Health and Supercomputing Research Group, Technological University of Panama, Panama City 0819-07289, Panama
José L. Castillo-Sequera: Department of Computer Science, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
Miguel Pablo Aguirre: Department of Electrical and Electronic Engineering, Technological Institute of Buenos Aires, Buenos Aires C1437FBG, Argentina
José Sanz-Moreno: Foundation for Biomedical Research, Hospital Príncipe de Asturias, 28805 Alcalá de Henares, Spain
María-Luz Polo-Luque: Ramón y Cajal Institute for Health Research, 28034 Madrid, Spain
Diego Rodríguez-Puyol: Department of Medicine and Medical Specialties, Research Foundation of the University Hospital Príncipe de Asturias, IRYCIS, Universidad de Alcalá, 28805 Alcalá de Henares, Spain

IJERPH, 2021, vol. 18, issue 24, 1-16

Abstract: 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.

Keywords: early diagnosis; infections; patients; machine learning; computer systems; internet use; cloud computing (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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