Learning to Identify Inappropriate Antimicrobial Prescriptions
Inappropriate antimicrobial prescribing is a major clinical problem and health concern. Several hospitals rely on automated surveillance to achieve hospital-wide antimicrobial optimization. The main challenge in implementing these systems lies in acquiring and updating their knowledge. In this paper, we discuss a surveillance system which can acquire new rules and improve its knowledge base. Our system uses an algorithm based on instance-based learning and rule induction to discover rules for inappropriate prescriptions. The algorithm uses temporal abstraction to extract a meaningful time interval representation from raw clinical data, and applies nearest neighbor classification with a distance function on both temporal and non-temporal parameters. The algorithm is able to discover new rules for early switch from intravenous to oral antimicrobial therapy from real clinical data.
KeywordsClassification temporal data mining interval sequence instance-based learning nearest-neighbor antimicrobial optimization
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