8 research outputs found
Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges
Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMsThis research work was partially supported by the Sejong University Research Faculty Program (20212023)S
Validation of design artefacts for blockchain-enabled precision healthcare as a service.
Healthcare systems around the globe are currently experiencing a rapid wave of digital disruption.
Current research in applying emerging technologies such as Big Data (BD), Artificial Intelligence
(AI), Machine Learning (ML), Deep Learning (DL), Augmented Reality (AR), Virtual Reality (VR),
Digital Twin (DT), Wearable Sensor (WS), Blockchain (BC) and Smart Contracts (SC) in contact
tracing, tracking, drug discovery, care support and delivery, vaccine distribution, management,
and delivery. These disruptive innovations have made it feasible for the healthcare industry to
provide personalised digital health solutions and services to the people and ensure sustainability
in healthcare. Precision Healthcare (PHC) is a new inclusion in digital healthcare that can support
personalised needs. It focuses on supporting and providing precise healthcare delivery. Despite
such potential, recent studies show that PHC is ineffectual due to the lower patient adoption in
the system. Anecdotal evidence shows that people are refraining from adopting PHC due to
distrust.
This thesis presents a BC-enabled PHC ecosystem that addresses ongoing issues and challenges
regarding low opt-in. The designed ecosystem also incorporates emerging information
technologies that are potential to address the need for user-centricity, data privacy and security,
accountability, transparency, interoperability, and scalability for a sustainable PHC ecosystem.
The research adopts Soft System Methodology (SSM) to construct and validate the design artefact
and sub-artefacts of the proposed PHC ecosystem that addresses the low opt-in problem.
Following a comprehensive view of the scholarly literature, which resulted in a draft set of design
principles and rules, eighteen design refinement interviews were conducted to develop the
artefact and sub-artefacts for design specifications. The artefact and sub-artefacts were validated
through a design validation workshop, where the designed ecosystem was presented to a Delphi
panel of twenty-two health industry actors. The key research finding was that there is a need for
data-driven, secure, transparent, scalable, individualised healthcare services to achieve
sustainability in healthcare. It includes explainable AI, data standards for biosensor devices,
affordable BC solutions for storage, privacy and security policy, interoperability, and usercentricity,
which prompts further research and industry application. The proposed ecosystem is
potentially effective in growing trust, influencing patients in active engagement with real-world
implementation, and contributing to sustainability in healthcare
Development of an Architecture for a Tele-Medicine-Based Longterm Monitoring System
Every day gigantic amounts of digital data are produced by billions of devices around the globe. Using this kind of data and develop applications of unlimited possibilities have created the Internet of Things (IoT) idea. Furthermore, wearable devices have taken up the recognition not only for private users, but also medical device producers and start-up companies. They have realized the potential of wearables in medical applications and their importance for the future of tele-medical systems, when being combined with an IoT based architecture. Despite the development of recent tele medicine platforms, none has used printed electronics to obtain physiological signals.
This thesis will provide a description of an architecture, that not only uses an IoT application as backbone, but also a hybrid printed electronics design for ECG and Bioimpedance Pneumography measurements. The recorded bio-signals are transferred via Bluetooth Low Energy to a mobile gateway and then onto a server. On the server the data will be processed in order to obtain features of each signal that provide significant information about the patient’s health. Finally this data is stored in a backup system and can be viewed through a graphical user interface.
As this thesis is rather a literature review than an experimental work, there will be no methods segment. An extensive background with the state-of-the-art technologies will be provided. The description of the architecture, shows that all the principal layers of an IoT application are met. Issues that arise with the usage of these systems are critically evaluated. This is the basis for researchers in the DISSE (DISappering SEnsors) project, in order to enable them to see the overall picture around their work within the project
Actas de SABI2020
Los temas salientes incluyen un marcapasos pulmonar que promete complementar y eventualmente sustituir la conocida ventilación mecánica por presión positiva (intubación), el análisis de la marchaespontánea sin costosos equipamientos, las imágenes infrarrojas y la predicción de la salud cardiovascular en temprana edad por medio de la biomecánica arterial
eHealth in Chronic Diseases
This book provides a review of the management of chronic diseases (evaluation and treatment) through eHealth. Studies that examine how eHealth can help to prevent, evaluate, or treat chronic diseases and their outcomes are included