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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

S. E. Brossette
1   Department of Pathology and Department of Computer and Information Sciences, The University of Alabama at Birmingham, Birmingham, AL, USA
,
A. P. Sprague
1   Department of Pathology and Department of Computer and Information Sciences, The University of Alabama at Birmingham, Birmingham, AL, USA
,
W. T. Jones
1   Department of Pathology and Department of Computer and Information Sciences, The University of Alabama at Birmingham, Birmingham, AL, USA
,
S. A. Moser
1   Department of Pathology and Department of Computer and Information Sciences, The University of Alabama at Birmingham, Birmingham, AL, USA
› Author Affiliations
Further Information

Publication History

Publication Date:
08 February 2018 (online)

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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
 

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