Time series outlier analysis for model, data and human-induced risks in covid-19 symptoms detection

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dc.authorid0000-0002-6105-0787en_US
dc.authorid0000-0001-8113-6193en_US
dc.authorid0000-0002-7137-4881en_US
dc.contributor.authorKaya, Ahmet
dc.contributor.authorGümüş, Rojan
dc.contributor.authorAydın, Ömer
dc.date.accessioned2022-11-02T07:21:21Z
dc.date.available2022-11-02T07:21:21Z
dc.date.issued2021en_US
dc.departmentDicle Üniversitesi, Atatürk Sağlık Hizmetleri Meslek Yüksek Okulu, Sağlık Bakım Hizmetleri Bölümüen_US
dc.description.abstractInformation systems are important references aiming to support the decisions of decision makers. Information reliability depends on the accuracy and efficacy of data and models. Therefore,some risks may emerge in information systems concerning models, data, and humans. It is important toidentify and extract outliers in decision support systems developed for the health information systemssuch as the detection system of Covid-19 symptoms. In this study, the risks that are important in decisionmaking in Covid-19 symptom detection were determined by the statistical time series (ARMA) approach.Potential solutions are proposed in this way. Moreover, outliers are detected by software developed byusing the Box-Jenkins model, and the reliability and accuracy of data are increased by using estimateddata instead of outliers. In the implementation of this study, time-series-based data obtained fromlaboratory examinations of Covid-19 test devices can be used. With the method revealed here, outliersoriginating from healthcare workers or test apparatus can be detected and more accurate results canbe obtained by replacing these outliers with estimated values.en_US
dc.identifier.citationKaya, A., Gümüş, R. ve Aydın, Ö. (2021). Time series outlier analysis for model, data and human-induced risks in covid-19 symptoms detection. Middle East Journal of Science, 7(2), 123-136.en_US
dc.identifier.doi10.51477/mejs.970510
dc.identifier.endpage136en_US
dc.identifier.issn2618-6136
dc.identifier.issue2en_US
dc.identifier.startpage123en_US
dc.identifier.trdizinid507513
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/507513
dc.identifier.urihttps://hdl.handle.net/11468/10716
dc.identifier.volume7en_US
dc.indekslendigikaynakTR-Dizin
dc.institutionauthorGümüş, Rojan
dc.language.isotren_US
dc.publisherInternational Engineering, Science and Education Groupen_US
dc.relation.ispartofMiddle East Journal of Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCovid-19en_US
dc.subjectHealth information systemsen_US
dc.subjectTime seriesen_US
dc.subjectOutlier analysisen_US
dc.subjectARMAen_US
dc.titleTime series outlier analysis for model, data and human-induced risks in covid-19 symptoms detectionen_US
dc.titleTime series outlier analysis for model, data and human-induced risks in covid-19 symptoms detection
dc.typeArticleen_US

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