Building predictive models for MERS-CoV infections using data mining techniques.
Identifieur interne : 002A30 ( PubMed/Checkpoint ); précédent : 002A29; suivant : 002A31Building predictive models for MERS-CoV infections using data mining techniques.
Auteurs : Isra Al-Turaiki [Arabie saoudite] ; Mona Alshahrani [Arabie saoudite] ; Tahani Almutairi [Arabie saoudite]Source :
- Journal of infection and public health [ 1876-035X ]
Descripteurs français
- KwdFr :
- MESH :
- anatomopathologie : Infections à coronavirus.
- mortalité : Infections à coronavirus.
- Adulte d'âge moyen, Analyse de survie, Arabie saoudite, Femelle, Fouille de données, Humains, Mâle, Pronostic, Résultat thérapeutique, Simulation numérique, Sujet âgé, Sujet âgé de 80 ans ou plus.
English descriptors
- KwdEn :
- MESH :
- mortality : Coronavirus Infections.
- pathology : Coronavirus Infections.
- Aged, Aged, 80 and over, Computer Simulation, Data Mining, Female, Humans, Male, Middle Aged, Prognosis, Saudi Arabia, Survival Analysis, Treatment Outcome.
Abstract
Recently, the outbreak of MERS-CoV infections caused worldwide attention to Saudi Arabia. The novel virus belongs to the coronaviruses family, which is responsible for causing mild to moderate colds. The control and command center of Saudi Ministry of Health issues a daily report on MERS-CoV infection cases. The infection with MERS-CoV can lead to fatal complications, however little information is known about this novel virus. In this paper, we apply two data mining techniques in order to better understand the stability and the possibility of recovery from MERS-CoV infections.
DOI: 10.1016/j.jiph.2016.09.007
PubMed: 27641481
Affiliations:
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Electronic address: ialturaiki@ksu.edu.sa.</nlm:affiliation><country xml:lang="fr">Arabie saoudite</country><wicri:regionArea>Information Technology Department, College of Computer and Information Sciences, King Saud University</wicri:regionArea><wicri:noRegion>King Saud University</wicri:noRegion></affiliation></author><author><name sortKey="Alshahrani, Mona" sort="Alshahrani, Mona" uniqKey="Alshahrani M" first="Mona" last="Alshahrani">Mona Alshahrani</name><affiliation wicri:level="1"><nlm:affiliation>Information Technology Department, College of Computer and Information Sciences, King Saud University, Saudi Arabia. Electronic address: monaalshahrani@outlook.com.</nlm:affiliation><country xml:lang="fr">Arabie saoudite</country><wicri:regionArea>Information Technology Department, College of Computer and Information Sciences, King Saud University</wicri:regionArea><wicri:noRegion>King Saud University</wicri:noRegion></affiliation></author><author><name sortKey="Almutairi, Tahani" sort="Almutairi, Tahani" uniqKey="Almutairi T" first="Tahani" last="Almutairi">Tahani Almutairi</name><affiliation wicri:level="1"><nlm:affiliation>Information Technology Department, College of Computer and Information Sciences, King Saud University, Saudi Arabia. Electronic address: 435203979@student.ksu.edu.sa.</nlm:affiliation><country xml:lang="fr">Arabie saoudite</country><wicri:regionArea>Information Technology Department, College of Computer and Information Sciences, King Saud University</wicri:regionArea><wicri:noRegion>King Saud University</wicri:noRegion></affiliation></author></analytic><series><title level="j">Journal of infection and public health</title><idno type="eISSN">1876-035X</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Aged</term><term>Aged, 80 and over</term><term>Computer Simulation</term><term>Coronavirus Infections (mortality)</term><term>Coronavirus Infections (pathology)</term><term>Data Mining</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Prognosis</term><term>Saudi Arabia</term><term>Survival Analysis</term><term>Treatment Outcome</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte d'âge moyen</term><term>Analyse de survie</term><term>Arabie saoudite</term><term>Femelle</term><term>Fouille de données</term><term>Humains</term><term>Infections à coronavirus (anatomopathologie)</term><term>Infections à coronavirus (mortalité)</term><term>Mâle</term><term>Pronostic</term><term>Résultat thérapeutique</term><term>Simulation numérique</term><term>Sujet âgé</term><term>Sujet âgé de 80 ans ou plus</term></keywords><keywords scheme="MESH" qualifier="anatomopathologie" xml:lang="fr"><term>Infections à coronavirus</term></keywords><keywords scheme="MESH" qualifier="mortality" xml:lang="en"><term>Coronavirus Infections</term></keywords><keywords scheme="MESH" qualifier="mortalité" xml:lang="fr"><term>Infections à coronavirus</term></keywords><keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Coronavirus Infections</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Aged, 80 and over</term><term>Computer Simulation</term><term>Data Mining</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Prognosis</term><term>Saudi Arabia</term><term>Survival Analysis</term><term>Treatment Outcome</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte d'âge moyen</term><term>Analyse de survie</term><term>Arabie saoudite</term><term>Femelle</term><term>Fouille de données</term><term>Humains</term><term>Mâle</term><term>Pronostic</term><term>Résultat thérapeutique</term><term>Simulation numérique</term><term>Sujet âgé</term><term>Sujet âgé de 80 ans ou plus</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Recently, the outbreak of MERS-CoV infections caused worldwide attention to Saudi Arabia. The novel virus belongs to the coronaviruses family, which is responsible for causing mild to moderate colds. The control and command center of Saudi Ministry of Health issues a daily report on MERS-CoV infection cases. The infection with MERS-CoV can lead to fatal complications, however little information is known about this novel virus. In this paper, we apply two data mining techniques in order to better understand the stability and the possibility of recovery from MERS-CoV infections.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">27641481</PMID><DateCompleted><Year>2017</Year><Month>03</Month><Day>13</Day></DateCompleted><DateRevised><Year>2020</Year><Month>04</Month><Day>01</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1876-035X</ISSN><JournalIssue CitedMedium="Internet"><Volume>9</Volume><Issue>6</Issue><PubDate><MedlineDate>2016 Nov - Dec</MedlineDate></PubDate></JournalIssue><Title>Journal of infection and public health</Title><ISOAbbreviation>J Infect Public Health</ISOAbbreviation></Journal><ArticleTitle>Building predictive models for MERS-CoV infections using data mining techniques.</ArticleTitle><Pagination><MedlinePgn>744-748</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S1876-0341(16)30146-0</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.jiph.2016.09.007</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Recently, the outbreak of MERS-CoV infections caused worldwide attention to Saudi Arabia. The novel virus belongs to the coronaviruses family, which is responsible for causing mild to moderate colds. The control and command center of Saudi Ministry of Health issues a daily report on MERS-CoV infection cases. The infection with MERS-CoV can lead to fatal complications, however little information is known about this novel virus. In this paper, we apply two data mining techniques in order to better understand the stability and the possibility of recovery from MERS-CoV infections.</AbstractText><AbstractText Label="METHOD" NlmCategory="METHODS">The Naive Bayes classifier and J48 decision tree algorithm were used to build our models. The dataset used consists of 1082 records of cases reported between 2013 and 2015. In order to build our prediction models, we split the dataset into two groups. The first group combined recovery and death records. A new attribute was created to indicate the record type, such that the dataset can be used to predict the recovery from MERS-CoV. The second group contained the new case records to be used to predict the stability of the infection based on the current status attribute.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">The resulting recovery models indicate that healthcare workers are more likely to survive. This could be due to the vaccinations that healthcare workers are required to get on regular basis. As for the stability models using J48, two attributes were found to be important for predicting stability: symptomatic and age. Old patients are at high risk of developing MERS-CoV complications. Finally, the performance of all the models was evaluated using three measures: accuracy, precision, and recall. In general, the accuracy of the models is between 53.6% and 71.58%.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">We believe that the performance of the prediction models can be enhanced with the use of more patient data. As future work, we plan to directly contact hospitals in Riyadh in order to collect more information related to patients with MERS-CoV infections.</AbstractText><CopyrightInformation>Copyright © 2016 King Saud Bin Abdulaziz University for Health Sciences. Published by Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Al-Turaiki</LastName><ForeName>Isra</ForeName><Initials>I</Initials><AffiliationInfo><Affiliation>Information Technology Department, College of Computer and Information Sciences, King Saud University, Saudi Arabia. Electronic address: ialturaiki@ksu.edu.sa.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Alshahrani</LastName><ForeName>Mona</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Information Technology Department, College of Computer and Information Sciences, King Saud University, Saudi Arabia. Electronic address: monaalshahrani@outlook.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Almutairi</LastName><ForeName>Tahani</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Information Technology Department, College of Computer and Information Sciences, King Saud University, Saudi Arabia. Electronic address: 435203979@student.ksu.edu.sa.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>09</Month><Day>15</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>J Infect Public Health</MedlineTA><NlmUniqueID>101487384</NlmUniqueID><ISSNLinking>1876-0341</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000369" MajorTopicYN="N">Aged, 80 and over</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="Y">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000401" MajorTopicYN="Y">mortality</QualifierName><QualifierName UI="Q000473" MajorTopicYN="Y">pathology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D057225" MajorTopicYN="N">Data Mining</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011379" MajorTopicYN="N">Prognosis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012529" MajorTopicYN="N">Saudi Arabia</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016019" MajorTopicYN="N">Survival Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016896" MajorTopicYN="N">Treatment Outcome</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Classification</Keyword><Keyword MajorTopicYN="Y">Data mining</Keyword><Keyword MajorTopicYN="Y">Decision tree</Keyword><Keyword MajorTopicYN="Y">J48</Keyword><Keyword MajorTopicYN="Y">MERS-CoV</Keyword><Keyword MajorTopicYN="Y">Naive Bayes</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2016</Year><Month>06</Month><Day>23</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2016</Year><Month>07</Month><Day>20</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2016</Year><Month>09</Month><Day>06</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2016</Year><Month>10</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2017</Year><Month>3</Month><Day>14</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2016</Year><Month>9</Month><Day>20</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">27641481</ArticleId><ArticleId IdType="pii">S1876-0341(16)30146-0</ArticleId><ArticleId IdType="doi">10.1016/j.jiph.2016.09.007</ArticleId><ArticleId IdType="pmc">PMC7102847</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>BMC Med Inform Decis Mak. 2012 Dec 07;12:143</Citation><ArticleIdList><ArticleId IdType="pubmed">23216895</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Glob J Health Sci. 2015 Jan 26;7(4):392-8</Citation><ArticleIdList><ArticleId IdType="pubmed">25946945</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>J Supercomput. 2016;72(8):3033-3056</Citation><ArticleIdList><ArticleId IdType="pubmed">32214655</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Arabie saoudite</li></country></list><tree><country name="Arabie saoudite"><noRegion><name sortKey="Al Turaiki, Isra" sort="Al Turaiki, Isra" uniqKey="Al Turaiki I" first="Isra" last="Al-Turaiki">Isra Al-Turaiki</name></noRegion><name sortKey="Almutairi, Tahani" sort="Almutairi, Tahani" uniqKey="Almutairi T" first="Tahani" last="Almutairi">Tahani Almutairi</name><name sortKey="Alshahrani, Mona" sort="Alshahrani, Mona" uniqKey="Alshahrani M" first="Mona" last="Alshahrani">Mona Alshahrani</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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