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Conference Papers Year : 2023

Progressive prediction of hospitalisation and patient disposition in the emergency department

Vincent Lemaire
  • Function : Author
Youenn Alexandre
  • Function : Author
Fanny Jardinaud
  • Function : Author
Paolo Bercelli
  • Function : Author
Saber Aloui
  • Function : Author

Abstract

Hospitals face high occupation rates resulting in a longer boarding time and more complex bed management. This task could be facilitated by anticipating the unscheduled admissions. We study the capability of information from French electronic health records of an emergency department (ED) to predict patient disposition decisions. We compare the performances of five learning models in predicting the admission of a patient visiting an emergency department and in predicting the patient’s place of admission at two progressive time points throughout the ED care process: triage and initial assessment. Medical and administrative data were retrospectively collected on 53,608 visits to the Groupe Hospitalier Bretagne Sud, France, from July 2020 to June 2021. Our best model achieve a ROCAUC equal to 88% and F1-score equal to 75% for admission prediction. Regarding medical unit admission prediction, the global ROC-AUC equals to 87% and F1-score ranges from 38% to 77% for the four admission classes, i.e., intensive care unit (6% of the dataset), medicine units (45%), surgery units (13.2%), and observation unit (35.8%). A validation with a posterior dataset indicates constant results.
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Dates and versions

hal-04408797 , version 1 (22-01-2024)

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Laura Uhl, Vincent Augusto, Vincent Lemaire, Youenn Alexandre, Fanny Jardinaud, et al.. Progressive prediction of hospitalisation and patient disposition in the emergency department. 2022 IEEE International Conference on Big Data (Big Data), Dec 2022, Osaka, Japan. pp.1719-1728, ⟨10.1109/BigData55660.2022.10020777⟩. ⟨hal-04408797⟩
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