Previously submitted to: JMIR mHealth and uHealth (no longer under consideration since Jul 24, 2020)
Date Submitted: Oct 14, 2019
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Cost-effective vital signs collecting system based on fast biosensors and a flexible Cloud microservices architecture for the prognosis of infectious diseases
ABSTRACT
Background:
Quite often, patients arrive to consultation when the symptoms of an infectious disease are already serious, forcing doctors to divert them to the emergency services. Particularly, the possible anticipation of the diagnosis -prognostic- for institutionalized people would lead to soften the treatment, increasing resident’s wellness and alleviating the degradation of the emergency services. Big data, mobile communications, cloud services or machine learning technologies applied in medicine -e-Health- assist practitioners with efficient tools.
Objective:
This article describes a new data collection system for predicting infectious diseases in elderly people, supporting future telecare and medical recommender applications.
Methods:
The system provides a medical database updated with vital signs that nurses take with medical sensors from residents. The Cloud database is accessible with a flexible microservices software architecture.
Results:
The e-Health system components are cost-effective, leading to massive implementations for servicing disadvantaged areas. The scalable architecture is prepared for big data applications that may extract valuable knowledge patterns for medical research.
Conclusions:
The innovation relies in the combination of advanced e-Health technologies and procedures that delivers ubiquitously available quality data to provide multifaceted scalable low-cost applications to improve resident’s wealth and release public health care services.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.