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Wearable Medical Sensor Devices, Machine and Deep Learning Algorithms, and Internet of Things-based Healthcare Systems in COVID-19 Patient Screening, Diagnosis, Monitoring, and Treatment

Wearable Medical Sensor Devices, Machine and Deep Learning Algorithms, and Internet of... The purpose of this study is to examine wearable medical sensor devices, machine and deep learning algorithms, and Internet of Things-based healthcare systems in COVID-19 patient screening, diagnosis, monitoring, and treatment. In this article, I cumulate previous research findings indicating that artificial intelligence tools can predict COVID-19 transmission patterns, assess disease severity, and predict mortality rate. I contribute to the literature on mobile medical applications and technologies by showing that Internet of Medical Things can save COVID- 19 diagnosis time and optimize physiological patient data collection by medical sensor devices. Throughout January 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “COVID-19” + “wearable medical sensor devices,” “machine and deep learning algorithms,” and “Internet of Things-based healthcare systems.” As I inspected research published between 2019 and 2022, only 144 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 28, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, MMAT, and SRDR. Keywords: Internet of Things; wearable medical sensor device; COVID-19 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Medical Research Addleton Academic Publishers

Wearable Medical Sensor Devices, Machine and Deep Learning Algorithms, and Internet of Things-based Healthcare Systems in COVID-19 Patient Screening, Diagnosis, Monitoring, and Treatment

Thomas Jenkins
American Journal of Medical Research , Volume 9 (1): 16 – Jan 1, 2022
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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
2334-4814
eISSN
2376-4481
Publisher site
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Abstract

The purpose of this study is to examine wearable medical sensor devices, machine and deep learning algorithms, and Internet of Things-based healthcare systems in COVID-19 patient screening, diagnosis, monitoring, and treatment. In this article, I cumulate previous research findings indicating that artificial intelligence tools can predict COVID-19 transmission patterns, assess disease severity, and predict mortality rate. I contribute to the literature on mobile medical applications and technologies by showing that Internet of Medical Things can save COVID- 19 diagnosis time and optimize physiological patient data collection by medical sensor devices. Throughout January 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “COVID-19” + “wearable medical sensor devices,” “machine and deep learning algorithms,” and “Internet of Things-based healthcare systems.” As I inspected research published between 2019 and 2022, only 144 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 28, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, MMAT, and SRDR. Keywords: Internet of Things; wearable medical sensor device; COVID-19

Journal

American Journal of Medical Research – Addleton Academic Publishers

Published: Jan 1, 2022

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