Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/23938

TitlePredict sepsis level in intensive medicine : data mining approach
Author(s)Gonçalves, João
Portela, Filipe
Santos, Manuel Filipe
Silva, Álvaro
Machado, José Manuel
Abelha, António
KeywordsData mIning
Classification
Intensive care
Sepsis
INTCare
Issue dateApr-2013
PublisherSpringer
JournalAdvances in Intelligent Systems and Computing
Abstract(s)This paper aims to support doctor’s decision-making on predicting the Sepsis level. Thus, a set of Data Mining (DM) models were developed using prevision techniques and classification models. These models enable a better doctor’s decision having into account the Sepsis level of the patient. The DM models use real data collected from the Intensive Care Unit of the Santo António Hospital, in Oporto, Portugal. Classification DM models were considered to predict sepsis level in a supervised learning approach. The models were induced making use of the following algorithms: Decision Trees, Support Vector Machines and Naïve Bayes classifier. The models were assessed using the Confusion Matrix, associated metrics, and Cross-validation. The analysis of the total error rate, sensitivity, specificity and accuracy were the metrics used to identify the most relevant measures to predict sepsis level. This work demonstrates that it is possible to predict with great accuracy the sepsis level.
TypeConference paper
URIhttp://hdl.handle.net/1822/23938
ISBN978-3-642-36981-0
DOI10.1007/978-3-642-36981-0_19
ISSN2194-5357
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals
CCTC - Artigos em atas de conferências internacionais (texto completo)

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