Bringing intelligence to the edge: towards smart and adaptive residential learning healthcare systems

Fares, N. (2024) Bringing intelligence to the edge: towards smart and adaptive residential learning healthcare systems. PhD thesis, University of Reading. doi: 10.48683/1926.00117047

Abstract/Summary

In light of an increasingly aging population, it becomes crucial to preserve good health and independence for as extended a period as feasible. Rather than hospitalization and placement in institutional care, individuals facing chronic illnesses or physical limitations can benefit from the support of smart healthcare Information and Communication Technology (ICT) solutions, right in the comfort of their own homes. Currently, there is a shift in healthcare ICT solutions towards more balanced approaches that integrate hospitals and homes, with the ultimate goal of transitioning to home-centric healthcare systems in the future. However, for this evolution to progress, it necessitates the integration of new technologies, system architectures, and computing paradigms. As this transformation advances towards the concept of Learning Healthcare Systems (LHS) it brings forth fresh challenges that must be addressed to meet evolving requirements. To enable the development of home-centric healthcare solutions it is essential to extend the concept of LHS to personal residences by incorporating intelligent Edge computing. A gateway is a key component in residential healthcare systems. By enhancing its adaptability and imbuing it with intelligence, we can elevate residential healthcare systems to the status of Learning Healthcare Systems (LHSs), capable of making real-time decisions. To develop adaptable consumer gateways for consumer healthcare applications, this research work outlines a set of technical requirements concerning scalability, reliability, availability, interoperability, energy efficiency, and privacy that need to be fulfilled before any product or service can be created. This research work aims to provide the requirements for the innovation of a one-for-all smart adaptive consumer gateway in residential learning healthcare systems and to influence the consumer healthcare field to consider the benefits of moving to adaptive gateways for future developments.

Item Type	Thesis (PhD)
URI	https://reading-clone.eprints-hosting.org/id/eprint/117047
Identification Number/DOI	10.48683/1926.00117047
Divisions	Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Date on Title Page	October 2023
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