Machine Learning for Emergency Department Management

Sofia Benbelkacem, Farid Kadri, Baghdad Atmani and Sondès Chaabane
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Sofia Benbelkacem: Laboratoire d'Informatique d'Oran (LIO), University of Oran 1 Ahmed Ben Bella, Algeria
Farid Kadri: Big Data & Analytics Services, Institut d'Optique Graduate School, Talence, France
Baghdad Atmani: Laboratoire d'Informatique d'Oran (LIO), University of Oran 1 Ahmed Ben Bella, Algeria
Sondès Chaabane: University Polytechnique Hauts-de-France, CNRS, UMR 8201 – LAMIH, Laboratoire d'Automatique de Mécanique et d'Informatique Industrielles et Humaines, F-59313 Valenciennes, France

International Journal of Information Systems in the Service Sector (IJISSS), 2019, vol. 11, issue 3, 19-36

Abstract: Nowadays, emergency department services are confronted to an increasing demand. This situation causes emergency department overcrowding which often increases the length of stay of patients and leads to strain situations. To overcome this issue, emergency department managers must predict the length of stay. In this work, the researchers propose to use machine learning techniques to set up a methodology that supports the management of emergency departments (EDs). The target of this work is to predict the length of stay of patients in the ED in order to prevent strain situations. The experiments were carried out on a real database collected from the pediatric emergency department (PED) in Lille regional hospital center, France. Different machine learning techniques have been used to build the best prediction models. The results seem better with Naive Bayes, C4.5 and SVM methods. In addition, the models based on a subset of attributes proved to be more efficient than models based on the set of attributes.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jisss0:v:11:y:2019:i:3:p:19-36

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