Machine learning approach for dosage individualization of azithromycin in children with community‐acquired pneumonia

AIMS: of azithromycin in children with community-acquired pneumonia. METHODS: ) became available, a posteriori-ML model was built for improved prediction. Statistical methods and pharmacodynamic (PD) evaluation methods were used to evaluate the ML model's predictive accuracy in a real-world study. ML-optimized doses were evaluated by calculating the probability of PD target attainment in virtual trials compared with guideline-recommended doses. RESULTS: as a predictor. In real-world validation, the mean absolute prediction error of the priori-ML and posteriori-ML models was less than 30%. The accuracy (determining whether the PD target is met) of the priori-ML model was 76.3%, whereas that of the posteriori-ML model increased to 90.4%. CONCLUSIONS: .