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Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?


Ribers, Michael A.; Ullrich, Hannes (2019). Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing? SSRN 3392196, University of Zurich.

Abstract

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.

Abstract

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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Additional indexing

Item Type:Working Paper
Communities & Collections:03 Faculty of Economics > Department of Business Administration
Dewey Decimal Classification:330 Economics
Language:English
Date:2019
Deposited On:31 Jan 2020 13:06
Last Modified:31 Jan 2020 13:07
Series Name:SSRN
ISSN:1556-5068
OA Status:Green
Publisher DOI:https://doi.org/10.2139/ssrn.3392196
Related URLs:https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3392196
Other Identification Number:merlin-id:18825

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