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.
Highlights
Background and ObjectivesThe world’s older population is growing at a significant rate
Three particular infectious diseases were considered: acute respiratory, urinary tract, and skin and soft tissue infections; a customized biosensor system was developed for the project; the communications infrastructure for data collection, storage, and analysis ws based on microservices; and machine learning algorithms were integrated into the microservices for prediction purposes
Some unsupervised learning techniques used in eHealth are K-means, density-based spatial clustering of applications with noise (DBSCAN), self-organized maps (SOMS), similarity network fusion (SNF), perturbation clustering for data integration and disease subtyping (PINS), and cancer integration via multikernel learning (CIMLR), among others
Summary
The world’s older population is growing at a significant rate. Today, 8.5% of the population is aged 65 and over; this will increase to 17% by 2050 [1]. This research presents the application of mobile communications, cloud services, and machine learning technology to provide efficient tools to medical staff in nursing homes in order to predict the development of infectious diseases. Three particular infectious diseases were considered: acute respiratory, urinary tract, and skin and soft tissue infections; a customized biosensor system was developed for the project; the communications infrastructure for data collection, storage, and analysis ws based on microservices; and machine learning algorithms were integrated into the microservices for prediction purposes To this end, vital signs from the residents were taken daily and transferred to a database in the cloud by means of an experimental data capture system. The infection-related inflammation process, stress induced activation of the sympathetic nervous system, and modifications of the activities of the nuclei that regulate heart and lung functioning may modify body temperature, electrodermal activity, oxygen saturation, heart beat rate, and blood pressure
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