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Automatic extraction of knowledge for diagnosing COVID-19 disease based on text mining techniques: A systematic review

In late December 2019, an epidemic of the novel coronavirus (COVID-19) was informed, and because of the quick diffusion of the infection in various regins of the world, the World Health Organization proclaimed an emergency. In this context, researchers are urged and encouraged to research in various...

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Main Authors: Mahdi, Amir Yasseen, Siti Sophiayati Yuhaniz, Siti Sophiayati Yuhaniz
Format: Article
Language:English
Published: International University of Sarajevo 2021
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/97478/1/SitiSophiayatiYuhaniz2021_AutomaticExtractionOfKnowledgeForDiagnosingCOVID19.pdf
http://eprints.utm.my/id/eprint/97478/
http://dx.doi.org/10.21533/pen.v9i2.1945
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spelling my.utm.974782022-10-17T03:36:07Z http://eprints.utm.my/id/eprint/97478/ Automatic extraction of knowledge for diagnosing COVID-19 disease based on text mining techniques: A systematic review Mahdi, Amir Yasseen Siti Sophiayati Yuhaniz, Siti Sophiayati Yuhaniz T Technology (General) In late December 2019, an epidemic of the novel coronavirus (COVID-19) was informed, and because of the quick diffusion of the infection in various regins of the world, the World Health Organization proclaimed an emergency. In this context, researchers are urged and encouraged to research in various fields, to stop the spread of this deadly virus. To this end, we propose a systematic review that addresses the techniques and methods of artificial intelligence in diagnosing COVID-19 disease. The main aim of the current systematic review was to highlight the gaps and challenges within the academic literature of the disease COVID-19, which included the characteristics of the data, machine learning algorithms applied to the diagnosis of COVID-19, and using natural language processing (NLP)to reveal clinical data for COVID-19 disease.Seven reliable databases were used, namely Web of Science, ScienceDirect, IEEE Xplore, Scopus, PubMed, springer and google scholar, to obtain studies related to the specific topic many filtering and surveying stages were conducted consistent with the inclusion and exclusion criteria, to screen the acquired 1115 papers.We identified the bottleneck in explaining data as one of the major barriers to machine learning and NLP approaches. Supervised machine learning has been explored as an active method for diagnosing COVID-19 disease. Future studies in this area will benefit from alternatives like increasing the volume of data, using intelligence swarms to obtain accurate features, and using unsupervised learning that does not require explanatory data. Thus, this research supported us to get a more practical comprehension of the gaps and provide possible solutions for filling these gaps. International University of Sarajevo 2021 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/97478/1/SitiSophiayatiYuhaniz2021_AutomaticExtractionOfKnowledgeForDiagnosingCOVID19.pdf Mahdi, Amir Yasseen and Siti Sophiayati Yuhaniz, Siti Sophiayati Yuhaniz (2021) Automatic extraction of knowledge for diagnosing COVID-19 disease based on text mining techniques: A systematic review. Periodicals of Engineering and Natural Sciences, 9 (2). pp. 918-929. ISSN 2303-4521 http://dx.doi.org/10.21533/pen.v9i2.1945 DOI : 10.21533/pen.v9i2.1945
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Mahdi, Amir Yasseen
Siti Sophiayati Yuhaniz, Siti Sophiayati Yuhaniz
Automatic extraction of knowledge for diagnosing COVID-19 disease based on text mining techniques: A systematic review
description In late December 2019, an epidemic of the novel coronavirus (COVID-19) was informed, and because of the quick diffusion of the infection in various regins of the world, the World Health Organization proclaimed an emergency. In this context, researchers are urged and encouraged to research in various fields, to stop the spread of this deadly virus. To this end, we propose a systematic review that addresses the techniques and methods of artificial intelligence in diagnosing COVID-19 disease. The main aim of the current systematic review was to highlight the gaps and challenges within the academic literature of the disease COVID-19, which included the characteristics of the data, machine learning algorithms applied to the diagnosis of COVID-19, and using natural language processing (NLP)to reveal clinical data for COVID-19 disease.Seven reliable databases were used, namely Web of Science, ScienceDirect, IEEE Xplore, Scopus, PubMed, springer and google scholar, to obtain studies related to the specific topic many filtering and surveying stages were conducted consistent with the inclusion and exclusion criteria, to screen the acquired 1115 papers.We identified the bottleneck in explaining data as one of the major barriers to machine learning and NLP approaches. Supervised machine learning has been explored as an active method for diagnosing COVID-19 disease. Future studies in this area will benefit from alternatives like increasing the volume of data, using intelligence swarms to obtain accurate features, and using unsupervised learning that does not require explanatory data. Thus, this research supported us to get a more practical comprehension of the gaps and provide possible solutions for filling these gaps.
format Article
author Mahdi, Amir Yasseen
Siti Sophiayati Yuhaniz, Siti Sophiayati Yuhaniz
author_facet Mahdi, Amir Yasseen
Siti Sophiayati Yuhaniz, Siti Sophiayati Yuhaniz
author_sort Mahdi, Amir Yasseen
title Automatic extraction of knowledge for diagnosing COVID-19 disease based on text mining techniques: A systematic review
title_short Automatic extraction of knowledge for diagnosing COVID-19 disease based on text mining techniques: A systematic review
title_full Automatic extraction of knowledge for diagnosing COVID-19 disease based on text mining techniques: A systematic review
title_fullStr Automatic extraction of knowledge for diagnosing COVID-19 disease based on text mining techniques: A systematic review
title_full_unstemmed Automatic extraction of knowledge for diagnosing COVID-19 disease based on text mining techniques: A systematic review
title_sort automatic extraction of knowledge for diagnosing covid-19 disease based on text mining techniques: a systematic review
publisher International University of Sarajevo
publishDate 2021
url http://eprints.utm.my/id/eprint/97478/1/SitiSophiayatiYuhaniz2021_AutomaticExtractionOfKnowledgeForDiagnosingCOVID19.pdf
http://eprints.utm.my/id/eprint/97478/
http://dx.doi.org/10.21533/pen.v9i2.1945
_version_ 1748180465185980416
score 13.211869

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