Improved Principal Component Analysis for Anomaly Detection: Application to an Emergency Department
dc.contributor.author | Harrou, Fouzi | |
dc.contributor.author | Kadri, Farid | |
dc.contributor.author | Chaabane, Sondés | |
dc.contributor.author | Tahon, Christian | |
dc.contributor.author | Sun, Ying | |
dc.date.accessioned | 2015-07-07T07:29:29Z | |
dc.date.available | 2015-07-07T07:29:29Z | |
dc.date.issued | 2015-07-05 | |
dc.identifier.citation | Improved Principal Component Analysis for Anomaly Detection: Application to an Emergency Department 2015 Computers & Industrial Engineering | |
dc.identifier.issn | 03608352 | |
dc.identifier.doi | 10.1016/j.cie.2015.06.020 | |
dc.identifier.uri | http://hdl.handle.net/10754/559088 | |
dc.description.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. | |
dc.publisher | Elsevier BV | |
dc.relation.url | http://linkinghub.elsevier.com/retrieve/pii/S036083521500279X | |
dc.rights | NOTICE: 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.subject | Abnormal situation | |
dc.subject | Emergency department | |
dc.subject | Multivariate CUSUM | |
dc.subject | Statistical anomaly detection | |
dc.title | Improved Principal Component Analysis for Anomaly Detection: Application to an Emergency Department | |
dc.type | Article | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.contributor.department | Environmental Statistics Group | |
dc.contributor.department | Statistics Program | |
dc.identifier.journal | Computers & Industrial Engineering | |
dc.eprint.version | Post-print | |
dc.contributor.institution | LAMIH, UMR CNRS 8201, University of Valenciennes and Hainaut-Cambrésis, UVHC, Le Mont Houy, 59313 Valenciennes Cedex, France | |
kaust.person | Harrou, Fouzi | |
kaust.person | Sun, Ying | |
refterms.dateFOA | 2018-07-03T00:00:00Z | |
dc.date.published-online | 2015-07-05 | |
dc.date.published-print | 2015-10 |
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