A Data Mining System for Infection Control Surveillance

S. E. Brossette; A. P. Sprague; W. T. Jones; S. A. Moser

doi:10.1055/s-0038-1634449

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 2000; 39(04/05): 303-310
DOI: 10.1055/s-0038-1634449

Original Article

Schattauer GmbH

A Data Mining System for Infection Control Surveillance

Authors

S. E. Brossette

¹Department of Pathology and Department of Computer and Information Sciences, The University of Alabama at Birmingham, Birmingham, AL, USA
A. P. Sprague

¹Department of Pathology and Department of Computer and Information Sciences, The University of Alabama at Birmingham, Birmingham, AL, USA
W. T. Jones

¹Department of Pathology and Department of Computer and Information Sciences, The University of Alabama at Birmingham, Birmingham, AL, USA
S. A. Moser

¹Department of Pathology and Department of Computer and Information Sciences, The University of Alabama at Birmingham, Birmingham, AL, USA

Abstract

Abstract:

Nosocomial infections and antimicrobial resistance are problems of enormous magnitude that impact the morbidity and mortality of hospitalized patients as well as their cost of care. The Data Mining Surveillance System (DMSS) uses novel data mining techniques to discover unsuspected, useful patterns of nosocomial infections and antimicrobial resistance from the analysis of hospital laboratory data. This report details a mature version of DMSS as well as an experiment in which DMSS was used to analyze all inpatient culture data, collected over 15 months at the University of Alabama at Birmingham Hospital.

Keywords

Data Mining - Surveillance - Antibiotic Resistance - Infection Control

Volltext

Referenzen

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