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

• Autorzy: Sariyer Görkem , Taşar Ceren Öcal , Cepe Gizem Ersoy

Identyfikatory

DOI

10.1515/bams-2018-0044

• Języki publikacji: EN

Abstrakty

EN

Emergency 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.

Słowa kluczowe

EN

CART; ED-LOS; logistic regression; multilayer perceptron; random forest

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2019
• Tom: Vol. 15, no. 1
• Strony: art. no. 20180044
• Opis fizyczny: Bibliogr. 46 poz., rys., tab.

Twórcy

autor

Sariyer Görkem

• Department of Business Administration, Yaşar University, İzmir, Turkey

autor

Taşar Ceren Öcal

• Yasar University, Software Engineering, Agaclikli Yol No:35-37 Izmir, Turkey

autor

Cepe Gizem Ersoy

• Bakırçay University, İzmir, Turkey

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).