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TitlePredicting nosocomial infection by using data mining technologies
Author(s)Silva, Eva
Cardoso, Luciana
Portela, Filipe
Abelha, António
Santos, Manuel
Machado, José Manuel
KeywordsClinical decision support system
Data mining
Knowledge discovery in databases
Nosocomial infection
Issue date1-Jan-2015
JournalAdvances in Intelligent Systems and Computing
CitationSilva, E., Cardoso, L., Portela, F., Abelha, A., Santos, M. F., & Machado, J. (2015). Predicting nosocomial infection by using data mining technologies. In New Contributions in Information Systems and Technologies (pp. 189-198). Springer, Cham
Abstract(s)The existence of nosocomial infection prevision systems in healthcare environments can contribute to improve the quality of the healthcare institution and also to reduce the costs with the treatment of the patients that acquire these infections. The analysis of the information available allows to efficiently prevent these infections and to build knowledge that can help to identify their eventual occurrence. This paper presents the results of the application of predictive models to real clinical data. Good models, induced by the Data Mining (DM) classification techniques Support Vector Machines and Naïve Bayes, were achieved (sensitivities higher than 91.90%). Therefore, with these models that be able to predict these infections may allow the prevention and, consequently, the reduction of nosocomial infection incidence. They should act as a Clinical Decision Support System (CDSS) capable of reducing nosocomial infections and the associated costs, improving the healthcare and, increasing patients’ safety and well-being.
TypeConference paper
Publisher version
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Livros e capítulos de livros/Books and book chapters

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