@inbook{868e4507d7a54b5c95d3b00a2ef4c3f0,
title = "Novelty detection for identifying deterioration in emergency department patients",
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.",
keywords = "Novelty Detection, Support Vector Machines",
author = "Clifton, {David A.} and David Wong and Susannah Fleming and Wilson, {Sarah J.} and Rob Way and Richard Pullinger and Lionel Tarassenko",
year = "2011",
doi = "10.1007/978-3-642-23878-9_27",
language = "English",
isbn = "9783642238772",
volume = "6936",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "220--227",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}