Internet of Medical Things-based Clinical Decision Support Systems, Smart Healthcare Wearable Devices, and Machine Learning Algorithms in COVID-19 Prevention, Screening, Detection, Diagnosis, and Treatment
Internet of Medical Things-based Clinical Decision Support Systems, Smart Healthcare Wearable Devices, and Machine Learning Algorithms in COVID-19 Prevention, Screening, Detection, Diagnosis, and Treatment

Summary/Abstract: We draw on a substantial body of theoretical and empirical research on Internet of Medical Things-based clinical decision support systems, smart health- care wearable devices, and machine learning algorithms in COVID-19 prevention, screening, detection, diagnosis, and treatment. With increasing evidence of wearable Internet of Medical Things technologies, there is an essential demand for comprehending whether tracking infected patients by machine learning algorithms can prevent the spread of COVID-19 by processing and analyzing accurate data. In this research, prior findings were cumulated indicating that Internet of Medical Things-assisted cutting-edge biosensor technologies are pivotal in COVID-19 infection. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout February 2022, with search terms including “COVID-19” + “Internet of Medical Things-based clinical decision support systems,” “smart healthcare wearable devices,” and “machine learning algorithms.” As we analyzed research published in 2021 and 2022, only 141 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 25, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, ROBIS, and SRDR.

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