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Machine Learning for Emergency Department Management

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.
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https://hal-uphf.archives-ouvertes.fr/hal-03405401
Contributor : Frédéric Pruvost Connect in order to contact the contributor
Submitted on : Wednesday, October 27, 2021 - 11:27:09 AM
Last modification on : Friday, May 20, 2022 - 10:28:59 AM

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Sofia Benbelkacem, Farid Kadri, Baghdad Atmani, Sondès Chaabane. Machine Learning for Emergency Department Management. International Journal of Information Systems in the Service Sector, 2019, 11 (3), pp.19-36. ⟨10.4018/IJISSS.2019070102⟩. ⟨hal-03405401⟩

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