title: A data mining system for infection control surveillance.
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.
authors.affiliation: Department of Pathology, University of Alabama at Birmingham, USA.
authors.forename: S E
authors.initials: SE
authors.lastname: Brossette
authors.forename: A P
authors.initials: AP
authors.lastname: Sprague
authors.forename: W T
authors.initials: WT
authors.lastname: Jones
authors.forename: S A
authors.initials: SA
authors.lastname: Moser
mesh_terms: D000407:Alabama / Q000453:epidemiology; D001424:Bacterial Infections / Q000453:epidemiology* / Q000517:prevention & control; D020000:Decision Support Systems, Clinical; D004196:Disease Outbreaks / Q000517:prevention & control*; D004352:Drug Resistance, Microbial; D006801:Humans; D015994:Incidence; D017053:Infection Control / Q000379:methods*; D011159:Population Surveillance / Q000379:methods*