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This paper presents the preliminary results of an observational study into the use of novelty detection techniques for detecting physiological deterioration in vital-sign data acquired from Emergency Department (ED) patients. Such patients are typically in an acute condition with a significant chance of deteriorating during their stay in hospital. Existing methods for monitoring ED patients involve manual "early warning score" (EWS) systems based on heuristics in which clinicians calculate a score based on the patient vital signs. We investigate automated novelty detection methods to perform "intelligent" monitoring of the patient between manual observations, to provide early warning of patient deterioration. Analysis of the performance of classification systems for on-line novelty detection is not straightforward. We discuss the obstacles that must be considered when determining the efficacy of on-line classification systems, and propose metrics for evaluating such systems. © 2011 Springer-Verlag.

Original publication




Journal article


Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Publication Date



6936 LNCS


220 - 227