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How to Predict Patient Arrival in the Emergency Room

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Information Systems and Technologies (WorldCIST 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 468))

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Abstract

The emergency services of the hospitals are for the most part suffering, facing a constant increase in the number of patients without the financial means to follow. Part of the answer to this worrying situation lies in the optimization of existing resources, for which artificial intelligence techniques are proving promising. In this article, we evaluate this possibility in a concrete way, starting from real data and applying a comparative analysis of 4 state-of-the-art algorithms. An original way of selecting explanatory variables is applied, and also the hyperparameters of the algorithms are chosen with precision. The results show that the most powerful machine learning algorithms currently available are quite capable of making good predictions of the number of patients arriving at the emergency room, provided that they are well applied.

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Acknowlegement

This research work has been supported by the CHU of Besançon, France.

Author information

Authors and Affiliations

  1. Femto-ST Institute, UMR 6174 CNRS, Université de Bourgogne Franche-Comté, Besançon, France

    Christophe Guyeux & Jacques M. Bahi

Authors
  1. Christophe Guyeux
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  2. Jacques M. Bahi
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Corresponding author

Correspondence to Christophe Guyeux .

Editor information

Editors and Affiliations

  1. ISEG, Universidade de Lisboa, Lisbon, Portugal

    Alvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  4. DCT, Universidade Portucalense, Porto, Portugal

    Fernando Moreira

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Guyeux, C., Bahi, J.M. (2022). How to Predict Patient Arrival in the Emergency Room. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F. (eds) Information Systems and Technologies. WorldCIST 2022. Lecture Notes in Networks and Systems, vol 468. Springer, Cham. https://doi.org/10.1007/978-3-031-04826-5_59

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