Evidence map›Paper›PMID 38077551›Full record

ArticlePeerJ. Computer science2023

IoMT based smart healthcare system to control outbreaks of the COVID-19 pandemic.

Nouf Abdullah Almujally, Turki Aljrees, Muhammad Umer, Oumaima Saidani, Danial Hanif, Nihal Abuzinadah, Khaled Alnowaiser, Imran Ashraf

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Nouf Abdullah AlmujallyDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Turki AljreesDepartment College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin, Saudi Arabia.
Muhammad UmerDepartment of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
Oumaima SaidaniDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Danial HanifDepartment of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
Nihal AbuzinadahFaculty of Computer Science and Information Technology, King Abdulaziz University, Jeddah, Sauid Arabia.
Khaled AlnowaiserDepartment of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Imran AshrafInformation and Communication Engineering, Yeungnam University, Gyeongsan si, Daegu, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic caused millions of infections and deaths globally requiring effective solutions to fight the pandemic. The Internet of Things (IoT) provides data transmission without human intervention and thus mitigates infection chances. A road map is discussed in this study regarding the role of IoT applications to combat COVID-19. In addition, a real-time solution is provided to identify and monitor COVID-19 patients. The proposed framework comprises data collection using IoT-based devices, a health or quarantine center, a data warehouse for artificial intelligence (AI)-based analysis, and healthcare professionals to provide treatment. The efficacy of several machine learning models is also analyzed for the prediction of the severity level of COVID-19 patients using real-time IoT data and a dataset named 'COVID Symptoms Checker'. The proposed ensemble model combines random forest and extra tree classifiers using a soft voting criterion and achieves superior results with a 0.922 accuracy score. The use of IoT applications is found to support medical professionals in investigating the features of the contagious disease and support managing the COVID pandemic more efficiently.

Indexed as

Deep learningInternet of Medical Things (MIoT)IoT applicationSensorsSmart healthcare system

Identifiers

PMID38077551
PMCPMC10702750

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.