Machine learning for estimating and comparing clinical rules for treating diarrheal illness with antibiotics

Acute diarrheal disease is a leading cause of death in children under age 5, disproportionately impacting children in low-resource settings. Although many cases could respond to antibiotic treatment, the benefits of widely prescribing antibiotics must be weighed against the risks of antimicrobial resistance. These challenges motivate development of individualized treatment guidelines for diarrheal disease. In this study, we utilize a framework for creation and evaluation of individualized treatment rules that leverage diagnostic and clinical information to make treatment recommendations. In contrast to many applications of pipelines for creating and evaluating treatment rules, we (i) explicitly create rules that limit the proportion of children treated based on expected clinical benefit, to limit risks for overtreatment and the emergence of antimicrobial resistance, and (ii) propose methods to compare rules based on different sets of input covariates, allowing for quantification of the impact of measuring additional biomarkers in clinical settings. We use a nested cross-validation procedure with ensemble machine learning and doubly-robust estimation to derive, evaluate, and compare rules. We demonstrate that our proposed method yields appropriate inference in a realistic simulation study and apply our method to data from the AntiBiotics for Children with severe Diarrhea (ABCD) trial.