Abstract
We describe refinements to and new experimental applications of the Data Mining Surveillance System (DMSS), which uses a large electronic health-care database for monitoring emerging infections and antimicrobial resistance. For example, information from DMSS can indicate potentially important shifts in infection and antimicrobial resistance patterns in the intensive care units of a single health-care facility.
Highlights
We describe refinements to and new experimental applications of the Data Mining Surveillance System (DMSS), which uses a large electronic health-care database for monitoring emerging infections and antimicrobial resistance
We have defined a new exploratory data mining process for automatically identifying new, unexpected, and potentially interesting patterns in hospital infection control and public health surveillance data. This process, and the system based on it, Data Mining Surveillance System (DMSS), use association rules to represent outcomes and association rule confidences to monitor changes in the incidence of those outcomes over time
Through experiments with infection control data from the University of Alabama at Birmingham Hospital, we have demonstrated that DMSS can identify potentially interesting and previously unknown patterns
Summary
Want antibiotic sensitivity info on the right only. Source of infection is not an outcome. To generate an alert for an association rule r, DMSS first constructs a current window (wc) and a past window (wp) on the series of incidence proportions of r (wc[r,0], wp[r,0] from the algorithm in the Figure). It computes the cumulative incidence proportion for each window. If an alert is not generated, the set of current and past windows is formed (wc[r,1], wp[r,1] from the algorithm in the Figure), and the cumulative incidence proportions are compared. Current and past window pairs are generated by the algorithm in the Figure. The events identified by DMSS must be investigated by domain experts to determine
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