Learning to Identify Inappropriate Antimicrobial Prescriptions

  • Mathieu Beaudoin
  • Froduald Kabanza
  • Vincent Nault
  • Louis Valiquette
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7885)


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.


Classification temporal data mining interval sequence instance-based learning nearest-neighbor antimicrobial optimization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mathieu Beaudoin
    • 1
  • Froduald Kabanza
    • 1
  • Vincent Nault
    • 2
  • Louis Valiquette
    • 2
  1. 1.Dept. of Computer ScienceUniversité de SherbrookeCanada
  2. 2.Dept. of Microbiology and InfectiologyUniversité de SherbrookeCanada

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