Machine Learning for Emergency Department Management

Machine Learning for Emergency Department Management

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) and 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)
Copyright: © 2019 |Pages: 18
DOI: 10.4018/IJISSS.2019070102
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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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2. Machine Learning And Predicting Patient Los In Ed

In recent years, there has been a dramatic increase in medical data being collected (Hermon & Williams, 2014). Data sets are frequently characterized by incompleteness, incorrectness, inexactness and sparseness. These problems are quite common in the medical field. This field requires human experts with a high level of expertise and able to maintain a high degree of concentration. Therefore, the use of machine learning techniques becomes indispensable for the development of medical decision support tools that model expert behavior, clinical interpretation and analysis, and to save time for practitioners. Machine learning has been an active research field finding success in many different medical areas (Liu, Lei, Yin, Zhang, Naijun, & El-Darzi, 2006; Bolon-Canedo, Remeseiro, Alonso-Betanzos, & Campilho, 2016).

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