Evidence map›Paper›PMID 35812486›Full record

SynthesisFrontiers in public health2022

A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.

Shahab S Band, Sina Ardabili, Atefeh Yarahmadi, Bahareh Pahlevanzadeh, Adiqa Kausar Kiani, Amin Beheshti, Hamid Alinejad-Rokny, Iman Dehzangi, Arthur Chang, Amir Mosavi and 1 more

Open access · goldAbstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.4field-weighted citation impact, top 11% of its field
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

1 citing paper in PubMed, 17 citations in OpenAlex.

  1. Article
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

11 authors at 8 institutions in 6 countries.

Shahab S BandFuture Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan.
Sina ArdabiliDepartment of Informatics, J. Selye University, Komárom, Slovakia.
Atefeh YarahmadiFuture Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan.
Bahareh PahlevanzadehDepartment of Design and System Operations, Regional Information Center for Science and Technology (R.I.C.E.S.T.), Shiraz, Iran.
Adiqa Kausar KianiFuture Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan.
Amin BeheshtiDepartment of Computing, Macquarie University, Sydney, NSW, Australia.
Hamid Alinejad-RoknyBioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, U.N.S.W. Sydney, Sydney, NSW, Australia.
Iman DehzangiDepartment of Computer Science, Rutgers University, Camden, NJ, United States.
Arthur ChangBachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, Douliu, Taiwan.
Amir MosaviJohn von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary.
Massoud MoslehpourDepartment of Business Administration, College of Management, Asia University, Taichung, Taiwan.
National Yunlin University of Science and Technology · TWAsia University · TWMacquarie University · AURegional Information Center for Science and Technology · IRRutgers, The State University of New Jersey · USSelye János University · SKSlovak University of Technology in Bratislava · SKUNSW Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early diagnosis, prioritization, screening, clustering, and tracking of patients with COVID-19, and production of drugs and vaccines are some of the applications that have made it necessary to use a new style of technology to involve, manage, and deal with this epidemic. Strategies backed by artificial intelligence (A.I.) and the Internet of Things (IoT) have been undeniably effective to understand how the virus works and prevent it from spreading. Accordingly, the main aim of this survey is to critically review the ML, IoT, and the integration of IoT and ML-based techniques in the applications related to COVID-19, from the diagnosis of the disease to the prediction of its outbreak. According to the main findings, IoT provided a prompt and efficient approach to tracking the disease spread. On the other hand, most of the studies developed by ML-based techniques aimed at the detection and handling of challenges associated with the COVID-19 pandemic. Among different approaches, Convolutional Neural Network (CNN), Support Vector Machine, Genetic CNN, and pre-trained CNN, followed by ResNet have demonstrated the best performances compared to other methods.

Indexed as

COVID-19Internet of ThingsMachine LearningArtificial IntelligenceHumansNeural Networks, ComputerPandemicsSupport Vector Machinebig datacoronavirusCOVID-19deep learninginformation systemsinternet of medical thingsInternet of Things (IoT)machine learning

Identifiers

PMID35812486
PMCPMC9260273
OpenAlexW4283327404

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.