Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/8015

TitleRating organ failure via adverse events using data mining in the intensive care unit
Author(s)Silva, Álvaro
Cortez, Paulo
Santos, Manuel Filipe
Gomes, Lopes
Neves, José
KeywordsAdverse event
Artificial neural network
Critical care
Data mining
Multinomial logistic regression
Organ failure assessment
artificial neural networks
Issue date2008
PublisherElsevier
JournalArtificial Intelligence in Medicine
Citation"Artificial Intelligence in Medicine". ISSN 0933-3657. 43:3 (Jul. 2008) 179--193.
Abstract(s)The main intensive care unit (ICU) goal is to avoid or reverse the organ failure process by adopting a timely intervention. Within this context, early identification of organ impairment is a key issue. The sequential organ failure assessment (SOFA) is an expert-driven score that is widely used in European ICUs to quantify organ disorder. This work proposes a complementary data-driven approach based on adverse events, defined from commonly monitored biometrics. The aim is to 8. study the impact of these events when predicting the risk of ICU organ failure.
TypeArticle
URIhttp://hdl.handle.net/1822/8015
DOI10.1016/j.artmed.2008.03.010
ISSN0933-3657
Publisher versionhttp://www.sciencedirect.com/science/journal/09333657
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals
DSI - Engenharia da Programação e dos Sistemas Informáticos

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