Nsaif, MK, Mahdi, BA, Bahar Al-Mayouf, YR, Mahdi, OA, Aljaaf, AJ and Khan, S (2022) An online COVID-19 self-assessment framework supported by IoMT technology. Journal of Intelligent Systems, 30 (1). pp. 966-975. ISSN 0334-1860

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Abstract

As COVID-19 pandemic continued to propagate, millions of lives are currently at risk especially elderly, people with chronic conditions and pregnant women. Iraq is one of the countries affected by the COVID-19 pandemic. Currently, in Iraq, there is a need for a self-assessment tool to be available in hand for people with COVID-19 concerns. Such a tool would guide people, after an automated assessment, to the right decision such as seeking medical advice, self-isolate, or testing for COVID-19. This study proposes an online COVID-19 self-assessment tool supported by the internet of medical things (IoMT) technology as a means to fight this pandemic and mitigate the burden on our nation's healthcare system. Advances in IoMT technology allow us to connect all medical tools, medical databases, and devices via the internet in one collaborative network, which conveys real-time data integration and analysis. Our IoMT framework-driven COVID-19 self-assessment tool will capture signs and symptoms through multiple probing questions, storing the data to our COVID-19 patient database, then analyze the data to determine whether a person needs to be tested for COVID-19 or other actions may require to be taken. Further to this, collected data can be integrated and analyzed collaboratively for developing a national health policy and help to manage healthcare resources more efficiently. The IoMT framework-driven online COVID-19 self-assessment tool has a big potential to prevent our healthcare system from being overwhelmed using real-time data collection, COVID-19 databases, analysis, and management of people with COVID-19 concerns, plus providing proper guidance and course of action.

Item Type:	Article
Uncontrolled Keywords:	0801 Artificial Intelligence and Image Processing; 0806 Information Systems; 1702 Cognitive Sciences; Artificial Intelligence & Image Processing
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Computer Science & Mathematics
Publisher:	Walter de Gruyter GmbH
SWORD Depositor:	A Symplectic
Date Deposited:	29 Jun 2023 13:35
Last Modified:	29 Jun 2023 13:45
DOI or ID number:	10.1515/jisys-2021-0048
URI:	https://researchonline.ljmu.ac.uk/id/eprint/20143

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