Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/30782

TitlePervasive and intelligent decision support in Intensive Medicine – the complete picture
Author(s)Portela, Filipe
Santos, Manuel Filipe
Machado, José Manuel
Abelha, António
Silva, Álvaro
Rua, Fernando
KeywordsPervasive
Decision support
Data Mining
INTCare
Intensive Medicine
Issue date2014
PublisherSpringer
JournalLecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract(s)In the Intensive Care Units (ICU) it is notorious the high number of data sources available. This situation brings more complexity to the way of how a professional makes a decision based on information provided by those data sources. Normally, the decisions are based on empirical knowledge and common sense. Often, they don’t make use of the information provided by the ICU data sources, due to the difficulty in understanding them. To overcome these constraints an integrated and pervasive system called INTCare has been deployed. This paper is focused in presenting the system architecture and the knowledge obtained by each one of the decision modules: Patient Vital Signs, Critical Events, ICU Medical Scores and Ensemble Data Mining. This system is able to make hourly predictions in terms of organ failure and outcome. High values of sensitivity where reached, e.g. 97.95% for the cardiovascular system, 99.77% for the outcome. In addition, the system is prepared for tracking patients’ critical events and for evaluating medical scores automatically and in real-time.
TypeConference paper
DescriptionSeries : Lecture notes in computer science (LNCS), vol. 8649
URIhttp://hdl.handle.net/1822/30782
ISBN978-3-319-10264-1
DOI10.1007/978-3-319-10265-8_9
ISSN0302-9743
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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