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Automating the Calibration of a Neonatal Condition Monitoring System

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http://dx.doi.org/10.1007/978-3-642-22218-4_30
Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine
Subtitle of host publication13th Conference on Artificial Intelligence in Medicine, AIME 2011, Bled, Slovenia, July 2-6, 2011. Proceedings
EditorsMor Peleg, Nada Lavrac, Carlo Combi
PublisherSpringer-Verlag GmbH
Pages240-249
Number of pages10
DOIs
Publication statusPublished - 2011

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume6747
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Abstract

Condition monitoring of premature babies in intensive care can be carried out using a Factorial Switching Linear Dynamical System (FSLDS) [15]. A crucial part of training the FSLDS is the manual calibration stage, where an interval of normality must be identified for each baby that is monitored. In this paper we replace this manual step by using a classifier to predict whether an interval is normal or not. We show that the monitoring results obtained using automated calibration are almost as good as those using manual calibration.

    Research areas

  • switching linear dynamical system, logistic regression, decision tree, intensive care, Naïve Bayes, Condition monitoring

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