Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?
Publikation: Working paper › Forskning
Standard
Battling Antibiotic Resistance : Can Machine Learning Improve Prescribing? / Ribers, Michael A.; Ullrich, Hannes.
2019.Publikation: Working paper › Forskning
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
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 -
ID: 242774662