Syndromic surveillance systems aim to support early detection of salient disease
outbreaks, and to shed timely light on the size and spread of pandemic
outbreaks. They can also be used more generally to monitor disease trends
and provide reassurance that an outbreak has not occurred. One commonly
used technique for syndromic surveillance is concerned with classifying Emergency
Department data, such as chief complaints or triage notes, into a set of
pre-defined syndromic groups. This paper reports our findings on the investigation
of the utility and e↵ectiveness of two existing North American methods
for free-text chief complaint classification on a large data set of Australian
Emergency Department triage notes, collected from two hospitals in the state
of Victoria. To our knowledge, these methods have never before been analysed
and compared against each other for their applicability and e↵ectiveness on
free text chief complaint classification at this scale or in the Australian context.
History
Pagination
1-6
Location
Sydney, N.S.W.
Start date
2015-10-20
End date
2015-10-21
ISSN
1613-0073
Language
eng
Publication classification
E1.1 Full written paper - refereed, E Conference publication
Copyright notice
2015, The Authors
Editor/Contributor(s)
Barbuto K, Schaper L, Verspoor K
Title of proceedings
BigData 2015 : Proceedings of the Scientific Stream at Big Data in Health Analytics 2015
Event
Health Informatics Society of Australia. Conference (2015 : Sydney, N.S.W.)
Publisher
M. Jeusfeld c/o Redaktion Sun SITE, Informatik V, RWTH Aachen
Place of publication
Aachen, Germany
Series
Health Informatics Society of Australia Conference