Evidence map›Paper›PMID 33614401›Full record

ArticleResults in physics2021

Remote diagnostic and detection of coronavirus disease (COVID-19) system based on intelligent healthcare and internet of things.

S K Elagan, Sayed F Abdelwahab, E A Zanaty, Monagi H Alkinani, Hammad Alotaibi, Mohammed E A Zanaty

Open access · goldAbstract read
In one paragraph

Article in Results in physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 10% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Current state of diagnostic, screening and surveillance testing methods for COVID-19 from an analytical chemistry point of view.Microchemical journal : devoted to the application of microtechniques in all branches of science · 2021
    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

6 authors at 4 institutions in 2 countries.

S K ElaganDepartment of Mathematics and Statistics, College of Science, Taif University, P.O.Box 11099, Taif 21944, Saudi Arabia.
Sayed F AbdelwahabDivision of Pharmaceutical Microbiology, Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, P.O.Box 11099, Taif 21944, Saudi Arabia.
E A ZanatyComputer Science Dept., Faculty of Computers and Information, Sohag University, Sohag, Egypt.
Monagi H AlkinaniComputer Science and Artificial Intelligence Department, Faculty of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.
Hammad AlotaibiDepartment of Mathematics and Statistics, College of Science, Taif University, P.O.Box 11099, Taif 21944, Saudi Arabia.
Mohammed E A ZanatyComuncations Dept., Faculty of Engineering, Sohag University, Sohag, Egypt.
Sohag University · EGTaif University · SAJeddah University · SAMinia University · EG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we will propose a novel system for remote detecting COVID-19 patients based on artificial intelligence technology and internet of things (IoT) in order to stop the virus spreading at an early stage. In this work, we will focus on connecting several sensors to work together as a system that can discover people infected with the Coronavirus remotely, this will reduce the spread of the disease. The proposed system consists of several devices called

Indexed as

COVID-19IOTMedical dataMedical sensors

Identifiers

PMID33614401
PMCPMC7879050
OpenAlexW3131298847

What OpenQuestion holds

Textmetadata
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.