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Analysis of Saturation in the Emergency Department: A Data-Driven Queuing Model Using Machine Learning
Adrien Wartelle, Farah Mourad-Chehade, Farouk Yalaoui, David Laplanche, Stéphane Sanchez
88 - 92
10.3233/SHTI220402
Research Article
Studies in Health Technology and Informatics
Volume 294: Challenges of Trustable AI and Added-Value on Health
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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