Identifying Techniques and Models for COVID-19 Prediction

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Shamsabadi Ahmadreza; Mirzapour Pegah; Heydari Mohammad; Mojdeganlou Hengameh; Karimi Amirali; Pashaei Zahra; Qaderi Kowsar; Mirghaderi Peyman; Azadi Cheshmekabodi Hadiseh; Mehraeen Esmaeil; Seyed Alinaghi Seyed Ahmad

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Iranian Medical Council Year:2023 | Volume:6 | Issue:2 Page(s): 207-228

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

Identifying Techniques and Models for COVID-19 Prediction

Author(s)

Keywords

COVID-19Q4

DiagnosisQ4

PredictionQ4

SARS-CoV-2Q4

Abstract

Background: Technologies can predict various aspects of COVID-19, such as early Prediction of cases and those ‎at higher risks of severe disease. Predictions will yield numerous benefits and can result in a lower number of cases ‎and deaths. Herein, we aimed to review the published models and techniques that predict various ‎COVID-19 outcomes and identify their role in the management of the COVID-19. ‎ Methods: This study was a review identifying the Prediction models and techniques for management of the COVID-19. Web of Science, Scopus, and PubMed were searched from December 2019 until ‎September 4th, 2021. In addition, Google Scholar was also searched. ‎ Results: We have reviewed 59 studies. The authors reviewed Prediction techniques in COVID-19 disease ‎management. Studies in these articles have shown that in the section medical setting, most of the subjects were ‎inpatients. In the purpose of the Prediction section, mortality was also the most item. In the type of data/predict ‎section, basic patient information, demographic, and laboratory values were the most cases. Also, in the type of ‎technique section, logistic regression was the most item used. Training, internal and external validation, and cross-validation were among the issues raised in the type of validation section. ‎ Conclusion: Artificial intelligence and machine learning methods were found to be useful in disease control and ‎prevention. They accelerate the process of Diagnosis and move toward great progress in emergency ‎circumstances like the COVID-19 pandemic. ‎

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops