Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/31182

TitlePervasive ensemble data mining models to predict organ failure and patient outcome in intensive medicine
Author(s)Portela, Filipe
Santos, Manuel Filipe
Silva, Álvaro
Abelha, António
Machado, José Manuel
KeywordsData Mining
Intensive care
Organ failure
Patient outcome
INTCare
Ensemble
Real-time
Pervasive health care
Issue date2013
PublisherSpringer
JournalCommunications in Computer and Information Science
Abstract(s)The number of patients admitted in Intensive care units (ICU) with organ failure is significant. This type of situation is common in Intensive Medicine. Intensive medicine is a specific area of medicine with the objective to avoid organ failure and recover patients in seriously ill conditions. This type of problems can culminate in the death of patient. In order to help the intensive medicine professionals in the moment of they take the decision, a Pervasive Intelligent Decision Support System called INTCare were developed. INTCare uses ensemble data mining to predict the probability of occurring an organ failure or patient die in the next hour. With the purpose to obtain always the better results, a measure was implemented to assess the models quality. All the transforming process and model induction are performed automatically and in real-time. The ensembles use online-learning to improve their models. In this paper the ensemble approach was explored and the results were compared at level of sensitivity, specificity, accuracy and total error. After the analysis was possible conclude that the ensembles are a too valid option to help the decision process in intensive Medicine.
TypeConference paper
DescriptionSeries : Communications in computer and information science, vol. 415
URIhttp://hdl.handle.net/1822/31182
ISBN978-3-642-54104-9
DOI10.1007/978-3-642-54105-6_27
ISSN1865-0929
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CCTC - Artigos em atas de conferências internacionais (texto completo)

Files in This Item:
File Description SizeFormat 
LNCCIS.pdf
  Restricted access
864,76 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID