TY - UNPB

T1 - Battling Antibiotic Resistance

T2 - Can Machine Learning Improve Prescribing?

AU - Ribers, Michael A.

AU - Ullrich, Hannes

PY - 2019/5/29

Y1 - 2019/5/29

N2 - 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.

AB - 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.

KW - Faculty of Social Sciences

KW - antibiotic prescribing

KW - prediction policy

KW - machine learning

KW - expert decision-making

UR - http://www.mendeley.com/research/battling-antibiotic-resistance-machine-learning-improve-prescribing

U2 - 10.2139/ssrn.3392196

DO - 10.2139/ssrn.3392196

M3 - Working paper

T3 - DIW Berlin Discussion Paper

BT - Battling Antibiotic Resistance

ER -