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Introduction <p>Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the ED.</p> Aim <p>The main objective of this study was to build and test a prediction tool for ED admissions using artificial intelligence.</p> Methods <p>We performed a retrospective multicenter study in two French ED from January 1st, 2010 to December 31st, 2019.We tested several machine learning algorithms and compared the results.</p> Results <p>The arrival and departure times from the ED of 2 hospitals were collected from all consultations during the study period, then grouped into 87 600 one-hour slots. Through the development of two models (one for each location), we found that the XGBoost method with hyperparameter adaptations was the best, suggesting that the studied data could be predicted (mean absolute error) at 2.63 for Hospital 1 and 2.64 for Hospital 2).</p> Conclusions <p>This study ran the construction and validation of a powerful tool for predicting ED admissions in 2 French ED. This type of tool should be integrated into the overall organization of an ED, to optimize the resources of healthcare professionals.</p>

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Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月6日

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  • Cyrielle Brossard,
  • Christophe Goetz,
  • Pierre Catoire,
  • Lauriane Cipolat,
  • Christophe Guyeux,
  • Cédric Gil Jardine,
  • Mahuna Akplogan,
  • Laure Abensur Vuillaume

摘要

Introduction

Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the ED.

Aim

The main objective of this study was to build and test a prediction tool for ED admissions using artificial intelligence.

Methods

We performed a retrospective multicenter study in two French ED from January 1st, 2010 to December 31st, 2019.We tested several machine learning algorithms and compared the results.

Results

The arrival and departure times from the ED of 2 hospitals were collected from all consultations during the study period, then grouped into 87 600 one-hour slots. Through the development of two models (one for each location), we found that the XGBoost method with hyperparameter adaptations was the best, suggesting that the studied data could be predicted (mean absolute error) at 2.63 for Hospital 1 and 2.64 for Hospital 2).

Conclusions

This study ran the construction and validation of a powerful tool for predicting ED admissions in 2 French ED. This type of tool should be integrated into the overall organization of an ED, to optimize the resources of healthcare professionals.