Novelty detection for identifying deterioration in emergency department patients

David A. Clifton, David Wong, Susannah Fleming, Sarah J. Wilson, Rob Way, Richard Pullinger, Lionel Tarassenko

Research output: Chapter in Book/Conference proceedingChapterpeer-review

Abstract

This paper presents the preliminary results of an observa- tional 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 condi- tion with a significant chance of deteriorating during their stay in hospi- tal. 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 auto- mated novelty detection methods to perform “intelligent” monitoring of the patient between manual observations, to provide early warning of pa- tient deterioration. Analysis of the performance of classification systems for on-line novelty detection is not straightforward. We discuss the ob- stacles that must be considered when determining the efficacy of on-line classification systems, and propose metrics for evaluating such systems.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages220-227
Number of pages8
Volume6936
ISBN (Electronic)9783642238789
DOIs
Publication statusPublished - 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6936 LNCS

Keywords

  • Novelty Detection
  • Support Vector Machines

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