# Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?

## TL;DR

This paper explores how machine learning can optimize antibiotic prescribing for urinary tract infections, reducing unnecessary antibiotic use by predicting bacterial causes with high accuracy, thereby combating antibiotic resistance.

## Contribution

It introduces a machine learning-based policy framework that improves prescribing decisions and demonstrates a significant reduction in antibiotic use without compromising treatment effectiveness.

## Key findings

- Machine learning reduces antibiotic use by 7.42% in Denmark.
- Predictions enable delayed or immediate prescribing based on test outcomes.
- Results likely underestimate potential benefits in less conservative settings.

## Abstract

Antibiotic resistance constitutes a major health threat. Predicting bacterial causes of infections is key to reducing antibiotic misuse, a leading driver of antibiotic resistance. We train a machine learning algorithm on administrative and microbiological laboratory data from Denmark to predict diagnostic test outcomes for urinary tract infections. Based on predictions, we develop policies to improve prescribing in primary care, highlighting the relevance of physician expertise and policy implementation when patient distributions vary over time. The proposed policies delay antibiotic 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, this result is likely to be a lower bound of what can be achieved elsewhere.