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Analysis of Saturation in the Emergency Department: A Data-Driven Queuing Model Using Machine Learning

Abstract : Emergency department is a key component of the health system where the management of crowding situations is crucial to the well-being of patients. This study proposes a new machine learning methodology and a queuing network model to measure and optimize crowding through a congestion indicator, which indicates a real-time level saturation.
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https://hal-utt.archives-ouvertes.fr/hal-03688662
Contributor : Adrien Wartelle Connect in order to contact the contributor
Submitted on : Sunday, June 5, 2022 - 5:07:50 PM
Last modification on : Wednesday, August 31, 2022 - 6:55:54 PM

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Adrien Wartelle, Farah Mourad-Chehade, Farouk Yalaoui, David Laplanche, Stéphane Sanchez. Analysis of Saturation in the Emergency Department: A Data-Driven Queuing Model Using Machine Learning. IOS press EBooks, 2022, Studies in Health Technology and Informatics, ⟨10.3233/SHTI220402⟩. ⟨hal-03688662⟩

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