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Nosocomial Infection Prediction Using Data Mining Technologies

Nosocomial Infection Prediction Using Data Mining Technologies
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Author(s): Eva Silva (Universidade do Minho, Portugal), Luciana Cardoso (Universidade do Minho, Portugal), Ricardo Faria (Universidade do Minho, Portugal)and Manuel Filipe Santos (Universidade do Minho, Portugal)
Copyright: 2016
Pages: 20
Source title: Applying Business Intelligence to Clinical and Healthcare Organizations
Source Author(s)/Editor(s): José Machado (University of Minho, Portugal)and António Abelha (University of Minho, Portugal)
DOI: 10.4018/978-1-4666-9882-6.ch010

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Abstract

The existence of nosocomial infection prediction systems in healthcare environments can contribute to improve the quality of the healthcare institution. Also, can reduce the costs with the treatment of those patients. The analysis of the information available allows to efficiently prevent these infections and to build knowledge that can help to identify the eventual occurrence of nosocomial infections. Good models induced by the DM classification techniques SVM, DT and NB, were achieved (sensitivities higher than 91.90%). Therefore, this system is able to predict these infections consequently, reduce the nosocomial infection incidence. The platform developed presents important information, as well as supports healthcare professionals in their decisions, namely in planning infection prevention measures. So, the system acts as a CDSS capable of reducing nosocomial infections and the associated costs, improving the healthcare and, increasing patient's safety and well-being.

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