About: Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review
Creator	Al-Obaidi, Jameel Alamoodi, A Albahri, A Zaidan, A Zaidan, B Alwan, Jwan Hadi, Suha Hamid, Rula Khlaf, Jamal Mohammed, K Thabet, Eman Al, Jameel Al-Qays, & Almahdi, E Alsalem, M Madhloom, H
topic	covid:520fdf5c2737f0e661ac0160667e2eb720075abb#this
source	Medline; PMC
abstract	Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called ‘COVID-19’, as a ‘public health emergency of international concern’. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.
has issue date	2020-05-25(xsd:dateTime)
bibo:doi	10.1007/s10916-020-01582-x
bibo:pmid	32451808
has license	no-cc
sha1sum (hex)	520fdf5c2737f0e661ac0160667e2eb720075abb
schema:url	https://doi.org/10.1007/s10916-020-01582-x
resource representing a document's title	Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review
has PubMed Central identifier	PMC7247866
has PubMed identifier	32451808
schema:publication	J Med Syst
resource representing a document's body	covid:520fdf5c2737f0e661ac0160667e2eb720075abb#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/520fdf5c2737f0e661ac0160667e2eb720075abb
is schema:about of	named entity 'based' named entity 'extracted' named entity 'public health emergency' named entity 'gain' named entity 'technologies' named entity 'declared' named entity 'humans' named entity 'provide' named entity 'studies' named entity 'January 30' named entity 'researchers' named entity 'databases' named entity 'bats' named entity 'common' named entity 'caused' named entity 'provide' named entity 'solutions' named entity 'medical' named entity 'distributed' named entity 'World Health Organisation' named entity 'disease symptoms' named entity 'decision tree' named entity 'COVID' named entity 'diarrhoea' named entity 'MERS CoV' named entity 'decision tree' named entity 'case-based reasoning' named entity 'virus' named entity 'worldwide epidemic' named entity 'fever' named entity 'medical community' named entity 'unsupervised learning' named entity 'cloud-based' named entity 'MERS-CoV' named entity 'CoV' named entity 'high fever' named entity 'COVID-19' named entity 'epidemic disease' named entity 'common cold' named entity 'outbreak of MERS' named entity 'current dataset' named entity 'CoV' named entity 'medical applications' named entity 'data mining' named entity 'World Health Organisation' named entity 'Naive Bayes classifiers' named entity 'CoV' named entity 'COVID-19' named entity 'GPS' named entity 'livestock' named entity 'MERS-CoV' named entity 'MERS-CoV' named entity '3, 4' named entity 'virus' named entity 'English language' named entity 'decision tree' named entity 'epidemic diseases' named entity 'virus' named entity 'ScienceDirect' named entity 'synthetic data' named entity 'severe acute respiratory syndrome' named entity 'supervised learning' named entity 'PubMed' named entity 'artificial intelligence'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software