Use of data mining techniques to classify length of stay of emergency department patients

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dc.authoridsariyer, gorkem / 0000-0002-8290-2248
dc.authorscopusid57189867008
dc.authorscopusid57207862021
dc.authorscopusid57207862015
dc.authorwosidTasar, Ceren Ocal/AAA-4770-2019
dc.authorwosidsariyer, gorkem/AAA-1524-2019
dc.contributor.authorSarıyer, Görkem
dc.contributor.authorTaşar, Ceren Öcal
dc.contributor.authorCepe, Gizem Ersoy
dc.date.accessioned2022-02-15T16:58:27Z
dc.date.available2022-02-15T16:58:27Z
dc.date.issued2019
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractEmergency departments (EDs) are the largest departments of hospitals which encounter high variety of cases as well as high level of patient volumes. Thus, an efficient classification of those patients at the time of their registration is very important for the operations planning and management. Using secondary data from the ED of an urban hospital, we examine the significance of factors while classifying patients according to their length of stay. Random Forest, Classification and Regression Tree, Logistic Regression (LR), and Multilayer Perceptron (MLP) were adopted in the data set of July 2016, and these algorithms were tested in data set of August 2016. Besides adopting and testing the algorithms on the whole data set, patients in these sets were grouped into 21 based on the similarities in their diagnoses and the algorithms were also performed in these subgroups. Performances of the classifiers were evaluated based on the sensitivity, specificity, and accuracy. It was observed that sensitivity, specificity, and accuracy values of the classifiers were similar, where LR and MLP had somehow higher values. In addition, the average performance of the classifying patients within the subgroups outperformed the classifying based on the whole data set for each of the classifiers.en_US
dc.identifier.doi10.1515/bams-2018-0044
dc.identifier.issn1895-9091
dc.identifier.issn1896-530X
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85063092018en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1515/bams-2018-0044
dc.identifier.urihttps://hdl.handle.net/20.500.14034/408
dc.identifier.volume15en_US
dc.identifier.wosWOS:000462825800004en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWalter De Gruyter Gmbhen_US
dc.relation.journalBio-Algorithms And Med-Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCARTen_US
dc.subjectED-LOSen_US
dc.subjectlogistic regressionen_US
dc.subjectmultilayer perceptronen_US
dc.subjectrandom foresten_US
dc.subjectClassificationen_US
dc.subjectRegressionen_US
dc.subjectArrivalsen_US
dc.subjectMedicineen_US
dc.titleUse of data mining techniques to classify length of stay of emergency department patientsen_US
dc.typeArticleen_US

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