Evidence map›Paper›PMID 35885882›Full record

ArticleHealthcare (Basel, Switzerland)2022

New Method to Implement and Analysis of Medical System in Real Time.

Yahia Zakria Abd Elgawad, Mohamed I Youssef, Tarek Mahmoud Nasser, Amir Almslmany, Ahmed S I Amar, Abdelrhman Adel Mohamed, Naser Ojaroudi Parchin, Raed A Abd-Alhameed, Heba G Mohamed, Karim H Moussa

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Yahia Zakria Abd ElgawadCollege of Engineering, Al_Azhar University, Cairo 11651, Egypt.
Mohamed I YoussefCollege of Engineering, Al_Azhar University, Cairo 11651, Egypt.
Tarek Mahmoud NasserCollege of Engineering, Al_Azhar University, Cairo 11651, Egypt.
Amir AlmslmanyDept. of Electronics & Comm, Air Defence College, Alexandria University, Alexandria 21526, Egypt.
Ahmed S I AmarDept. of Electronics & Comm, Air Defence College, Alexandria University, Alexandria 21526, Egypt.ORCID 0000-0002-9294-349X
Abdelrhman Adel MohamedDept. of Electronics & Comm, Air Defence College, Alexandria University, Alexandria 21526, Egypt.
Naser Ojaroudi ParchinSchool of Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK.ORCID 0000-0001-8375-0269
Raed A Abd-AlhameedFaculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK.ORCID 0000-0003-2972-9965
Heba G MohamedDepartment of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.ORCID 0000-0002-0443-1049
Karim H MoussaSchool of Internet of Things, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.ORCID 0000-0002-1589-1104

Funding

Princess Nourah bint Abdulrahman University PNURSP2022TR140
6 · The paper itself

Abstract

The use of information technology and technological medical devices has contributed significantly to the transformation of healthcare. Despite that, many problems have arisen in diagnosing or predicting diseases, either as a result of human errors or lack of accuracy of measurements. Therefore, this paper aims to provide an integrated health monitoring system to measure vital parameters and diagnose or predict disease. Through this work, the percentage of various gases in the blood through breathing is determined, vital parameters are measured and their effect on feelings is analyzed. A supervised learning model is configured to predict and diagnose based on biometric measurements. All results were compared with the results of the Omron device as a reference device. The results proved that the proposed design overcame many problems as it contributed to expanding the database of vital parameters and providing analysis on the effect of emotions on vital indicators. The accuracy of the measurements also reached 98.8% and the accuracy of diagnosing COVID-19 was 64%. The work also presents a user interface model for clinicians as well as for smartphones using the Internet of things.

Indexed as

COVID-19fuzzy logicGSRheart rateIoTmachine learningmedical devicesmicrocontrollers

Identifiers

PMID35885882
PMCPMC9321202

What OpenQuestion holds

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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.