title: Computerized detection of nosocomial infections in newborns.
abstract: Hospital-acquired infections are responsible for an increase in patient mortality and costs. Their detection is essential to permit better infection control. We developed an expert system specifically to detect infections in pediatric patients. The expert system is implemented at LDS Hospital that has a level three newborn intensive care unit and well baby units. We describe how the knowledge base of the expert system was developed, implemented, and validated in a retrospective study. The results of the system were compared to manual reviewer results. The expert system had a sensitivity of 84.5% and specificity of 92.8% in detecting hospital-acquired infections when compared to a physician reviewer. The Cohen's kappa between the expert system and the physician reviewer was 0.62 (p < .001).
authors.affiliation: University of Utah, Salt Lake City 84112.
authors.forename: B H
authors.initials: BH
authors.lastname: Rocha
authors.forename: J C
authors.initials: JC
authors.lastname: Christenson
authors.forename: A
authors.initials: A
authors.lastname: Pavia
authors.forename: R S
authors.initials: RS
authors.lastname: Evans
authors.forename: R M
authors.initials: RM
authors.lastname: Gardner
mesh_terms: D001185:Artificial Intelligence; D002835:Christianity; D003428:Cross Infection / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D005103:Expert Systems; D016519:Hospitals, Religious; D006801:Humans; D007231:Infant, Newborn; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014589:Utah