Evidence map›Paper›PMID 36258910›Full record

ArticleTanaffos2022

Identification of Risk Factors for COVID-19-related Death using Machine Learning Methods.

Saeid Bashirian, Maryam Mohammadi-Khoshnoud, Salman Khazaei, Elham Talebighane, Fariba Keramat, Fatemeh Bahreini, Sepideh Zareeian, Ali Reza Soltanian

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PubMed Full text at PMC
In one paragraph

Article in Tanaffos, 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. Review
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.

Saeid BashirianSocial Determinants of Health Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
Maryam Mohammadi-KhoshnoudDepartment of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Salman KhazaeiHealth Science Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
Elham TalebighaneHamadan University of Medical Sciences, Hamadan, Iran.
Fariba KeramatDepartment of Infectious Disease, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran.
Fatemeh BahreiniDepartment of Molecular Medicine and Genetics, Faculty of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran.
Sepideh ZareeianHamadan University of Medical Sciences, Hamadan, Iran.
Ali Reza SoltanianModeling of Non-Communicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Unknown cases of pneumonia appeared in late 2019 in Wuhan, China. Following the worldwide spread of the disease, the World Health Organization declared it a pandemic on March 11, 2020. The total number of infected people worldwide as of December 16, 2020, was more than 74 million, more than one million and six hundred thousand of whom died from Coronavirus Disease 2019 (COVID-19). This study aimed to identify the risk factors for the mortality of COVID-19 in Hamadan, west of Iran. Materials and Methods: This cross-sectional study used the information of all patients with COVID-19 admitted to Shahid Beheshti and Sina hospitals in Hamadan during January 2020-November 2020. Logistic regression model, decision tree, and random forest were used to assess risk factors for death due to COVID-19. Results: This study was conducted on 1853 people with COVID-19. Blood urea nitrogen change, SPO Conclusion: The risk factors identified in the present study may serve as surrogate indicators to identify the risk of death due to COVID-19. The proper model to predict COVID-19-related mortality is random forest based on sensitivity.

Indexed as

Data miningEmerging diseaseMortalityRisk factorsSARS-CoV-2

Identifiers

PMID36258910
PMCPMC9571237

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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.