Personalized azithromycin treatment rules for children with watery diarrhea using machine learning.

ABSTRACT: We use machine learning to identify innovative strategies to target azithromycin to the children with watery diarrhea who are most likely to benefit. Using data from a randomized trial of azithromycin for watery diarrhea (NCT03130114), we develop personalized treatment rules given sets of diagnostic, child, and clinical characteristics, employing a robust ensemble machine learning-based procedure. This procedure estimates the child-level expected benefit for a given set of covariates by combining predictions from a library of statistical models. For each rule, we estimate the proportion treated under the rule and the average benefits of treatment. Among 6692 children, treatment under the most comprehensive rule is recommended on average for one third of children. The risk of diarrhea on da

SUBMITTER: Kim SS 

PROVIDER: S-EPMC12216316 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature