Improved Principal Component Analysis for Anomaly Detection: Application to an Emergency Department

Handle URI:
http://hdl.handle.net/10754/559088
Title:
Improved Principal Component Analysis for Anomaly Detection: Application to an Emergency Department
Authors:
Harrou, Fouzi; Kadri, Farid; Chaabane, Sondés; Tahon, Christian; Sun, Ying ( 0000-0001-6703-4270 )
Abstract:
Monitoring 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Improved Principal Component Analysis for Anomaly Detection: Application to an Emergency Department 2015 Computers & Industrial Engineering
Journal:
Computers & Industrial Engineering
Issue Date:
3-Jul-2015
DOI:
10.1016/j.cie.2015.06.020
Type:
Article
ISSN:
03608352
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S036083521500279X
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHarrou, Fouzien
dc.contributor.authorKadri, Fariden
dc.contributor.authorChaabane, Sondésen
dc.contributor.authorTahon, Christianen
dc.contributor.authorSun, Yingen
dc.date.accessioned2015-07-07T07:29:29Zen
dc.date.available2015-07-07T07:29:29Zen
dc.date.issued2015-07-03en
dc.identifier.citationImproved Principal Component Analysis for Anomaly Detection: Application to an Emergency Department 2015 Computers & Industrial Engineeringen
dc.identifier.issn03608352en
dc.identifier.doi10.1016/j.cie.2015.06.020en
dc.identifier.urihttp://hdl.handle.net/10754/559088en
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.en
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S036083521500279Xen
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.2015.06.020en
dc.subjectAbnormal situationen
dc.subjectEmergency departmenten
dc.subjectMultivariate CUSUMen
dc.subjectStatistical anomaly detectionen
dc.titleImproved Principal Component Analysis for Anomaly Detection: Application to an Emergency Departmenten
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalComputers & Industrial Engineeringen
dc.eprint.versionPost-printen
dc.contributor.institutionLAMIH, UMR CNRS 8201, University of Valenciennes and Hainaut-Cambrésis, UVHC, Le Mont Houy, 59313 Valenciennes Cedex, Franceen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
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