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Emergency Department Admissions Overflow Modeling by a Clustering of Time Evolving Clinical Diagnoses

Abstract : Emergency Department (ED) of hospitals are greatly impacted by winter epidemics due to respiratory diseases and patient flow has long been essential to detect the underlying overcrowding. In this paper we propose to model the admission flow corresponding to clinical diagnoses encoded with ICD-10 which are more likely linked with respiratory diseases. To achieve this, clustering algorithms are applied on time evolving diagnosis in the adult ED of Saint-Etienne and benchmarked regarding a time series of laboratory-confirmed influenza data. For both K-Means and Hierarchical algorithms, the cluster containing the laboratory-confirmed series is composed of ICD-10 codes representing respiratory diseases and diseases linked with cardiac disorders, showing that these diseases present similar variations overtime. The information contained in such a cluster allow to plot the average number of arrivals and the average length of stay of the patients in ED only with these diagnoses.
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Submitted on : Tuesday, October 2, 2018 - 2:41:45 PM
Last modification on : Saturday, September 24, 2022 - 2:28:05 PM
Long-term archiving on: : Thursday, January 3, 2019 - 3:18:41 PM

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  • HAL Id : hal-01885975, version 1

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Gregory Soler, Guillaume Bouleux, Eric Marcon, Aymeric Cantais, Sylvie Pillet, et al.. Emergency Department Admissions Overflow Modeling by a Clustering of Time Evolving Clinical Diagnoses. 14th IEEE International Conference on Automation Science and Engineering (CASE 2018), Aug 2018, Munich, Germany. ⟨hal-01885975⟩

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