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Predicting Nosocomial Infection by Using Data Mining Technologies

  • Eva SilvaEmail author
  • Luciana Cardoso
  • Filipe Portela
  • António Abelha
  • Manuel Filipe Santos
  • José Machado
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 354)

Abstract

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.

Keywords

Clinical Decision Support System CRISP-DM Data Mining Knowledge Discovery in Databases Nosocomial Infection 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Eva Silva
    • 1
    Email author
  • Luciana Cardoso
    • 1
  • Filipe Portela
    • 1
  • António Abelha
    • 1
  • Manuel Filipe Santos
    • 1
  • José Machado
    • 1
  1. 1.Algoritmi Research CentreUniversity of MinhoBragaPortugal

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