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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)

Abstract

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.

Keywords

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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