Enhanced monitoring of abnormal emergency department demands

Fouzi Harrou, Ying Sun, Farid Kadri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper presents a statistical technique for detecting signs of abnormal situation generated by the influx of patients at emergency department (ED). The monitoring strategy developed was able to provide early alert mechanisms in the event of abnormal situations caused by abnormal patient arrivals to the ED. More specifically, This work proposed the application of autoregressive moving average (ARMA) models combined with the generalized likelihood ratio (GLR) test for anomaly-detection. ARMA was used as the modelling framework of the ARMA-based GLR anomaly-detection methodology. The GLR test was applied to the uncorrelated residuals obtained from the ARMA model to detect anomalies when the data did not fit the reference ARMA model. The ARMA-based GLR hypothesis testing scheme was successfully applied to the practical data collected from the database of the pediatric emergency department (PED) at Lille regional hospital center, France. © 2015 IEEE.
Original languageEnglish (US)
Title of host publication2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages692-696
Number of pages5
ISBN (Print)9781467387095
DOIs
StatePublished - Jun 13 2016

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