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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?
Organization Unit
Authors
  • Michael A. Ribers
  • Hannes Ullrich
Language
  • English
Institution University of Zurich
Series Name SSRN
Number 3392196
ISSN 1556-5068
Date 2019
Abstract Text Antibiotic resistance constitutes a major health threat. Predicting bacterial causes of infections is key to reducing antibiotic misuse, a leading cause of antibiotic resistance. We combine administrative and microbiological laboratory data from Denmark to train a machine learning algorithm predicting bacterial causes of urinary tract infections. Based on predictions, we develop policies to improve prescribing in primary care, highlighting the relevance of physician expertise and time-variant patient distributions for policy implementation. The proposed policies delay prescriptions for some patients until test results are known and give them instantly to others. We find that machine learning can reduce antibiotic use by 7.42 percent without reducing the number of treated bacterial infections. As Denmark is one of the most conservative countries in terms of antibiotic use, targeting a 30 percent reduction in prescribing by 2020, this result is likely to be a lower bound of what can be achieved elsewhere.
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Digital Object Identifier 10.2139/ssrn.3392196
Other Identification Number merlin-id:18825
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