Evidence map›Paper›PMID 36320377›Full record

ArticleNetwork modeling and analysis in health informatics and bioinformatics2022

A comparison of Covid-19 cases and deaths in Turkey and in other countries.

Oğuzhan Çağlar, Figen Özen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Network modeling and analysis in health informatics and bioinformatics, 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
–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

1 citing paper in PubMed.

  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

2 authors.

Oğuzhan ÇağlarElectrical and Electronics Engineering Department, Haliç University, Mareşal Fevzi Çakmak Cad. No: 15, Güzeltepe Mah. Eyüp, 34060 Istanbul, Turkey.
Figen ÖzenElectrical and Electronics Engineering Department, Haliç University, Mareşal Fevzi Çakmak Cad. No: 15, Güzeltepe Mah. Eyüp, 34060 Istanbul, Turkey.ORCID 0000-0002-1759-0073

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, the characteristics of the Covid-19 pandemic in Turkey are examined in terms of the number of cases and deaths, and a characteristic prediction is made with an approach that employs artificial intelligence. The number of cases and deaths are estimated using the number of tests, the numbers of seriously ill and recovered patients as parameters. The machine learning methods used are linear regression, polynomial regression, support vector regression with different kernel functions, decision tree and artificial neural networks. The obtained results are compared by calculating the coefficient of determination (

Indexed as

Artificial neural networkCovid-19Decision treeLinear regressionPolynomial regressionSupport vector regression

Identifiers

PMID36320377
PMCPMC9612626

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.