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Published on: March 1, 2024
Using machine learning to examine drivers of inappropriate outpatient antibiotic prescribing in acute respiratory
Laura M King1, Michael Kusnetsov2, Avgoustinos Filippoupolitis2
1Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States.
Abstract:
Using a machine-learning model, we examined drivers of antibiotic prescribing for antibiotic-inappropriate acute respiratory illnesses in a large US claims data set. Antibiotics were prescribed in 11% of the 42 million visits in our sample. The model identified outpatient setting type, patient age mix, and state as top drivers of prescribing.
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