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

Adrien Wartelle (1, 2) , Farah Mourad-Chehade (2) , Farouk Yalaoui (2) , David Laplanche (1) , Stéphane Sanchez (1)

1 Centre hospitalier de Troyes
2 LOSI - Laboratoire d'Optimisation des Systèmes Industriels

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.

Domaines: Machine Learning [stat.ML]; Statistiques [stat]

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⟩