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dc.contributor.authorHarrou, Fouzi
dc.contributor.authorKadri, Farid
dc.contributor.authorChaabane, Sondés
dc.contributor.authorTahon, Christian
dc.contributor.authorSun, Ying
dc.date.accessioned2015-07-07T07:29:29Z
dc.date.available2015-07-07T07:29:29Z
dc.date.issued2015-07-05
dc.identifier.citationImproved Principal Component Analysis for Anomaly Detection: Application to an Emergency Department 2015 Computers & Industrial Engineering
dc.identifier.issn03608352
dc.identifier.doi10.1016/j.cie.2015.06.020
dc.identifier.urihttp://hdl.handle.net/10754/559088
dc.description.abstractMonitoring of production systems, such as those in hospitals, is primordial for ensuring the best management and maintenance desired product quality. Detection of emergent abnormalities allows preemptive actions that can prevent more serious consequences. Principal component analysis (PCA)-based anomaly-detection approach has been used successfully for monitoring systems with highly correlated variables. However, conventional PCA-based detection indices, such as the Hotelling’s T2T2 and the Q statistics, are ill suited to detect small abnormalities because they use only information from the most recent observations. Other multivariate statistical metrics, such as the multivariate cumulative sum (MCUSUM) control scheme, are more suitable for detection small anomalies. In this paper, a generic anomaly detection scheme based on PCA is proposed to monitor demands to an emergency department. In such a framework, the MCUSUM control chart is applied to the uncorrelated residuals obtained from the PCA model. The proposed PCA-based MCUSUM anomaly detection strategy is successfully applied to the practical data collected from the database of the pediatric emergency department in the Lille Regional Hospital Centre, France. The detection results evidence that the proposed method is more effective than the conventional PCA-based anomaly-detection methods.
dc.publisherElsevier BV
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S036083521500279X
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Computers & Industrial Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Industrial Engineering, 3 July 2015. DOI: 10.1016/j.cie.2015.06.020
dc.subjectAbnormal situation
dc.subjectEmergency department
dc.subjectMultivariate CUSUM
dc.subjectStatistical anomaly detection
dc.titleImproved Principal Component Analysis for Anomaly Detection: Application to an Emergency Department
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEnvironmental Statistics Group
dc.contributor.departmentStatistics Program
dc.identifier.journalComputers & Industrial Engineering
dc.eprint.versionPost-print
dc.contributor.institutionLAMIH, UMR CNRS 8201, University of Valenciennes and Hainaut-Cambrésis, UVHC, Le Mont Houy, 59313 Valenciennes Cedex, France
kaust.personHarrou, Fouzi
kaust.personSun, Ying
refterms.dateFOA2018-07-03T00:00:00Z
dc.date.published-online2015-07-05
dc.date.published-print2015-10


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