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

ABSTRACT:

Introduction

We used machine learning to identify novel strategies to target azithromycin to the children with watery diarrhea who are most likely to benefit.

Methods

Using data from a randomized trial of azithromycin for watery diarrhea, we developed personalized treatment rules given sets of diagnostic, child, and clinical characteristics, employing a robust ensemble machine learning-based procedure. For each rule, we estimated the proportion treated under the rule and the average benefits of treatment.

Results

Among 6,692 children, treatment was recommended on average for approximately one third of children. The risk of diarrhea on day 3 was 10.1% lower (95% CI: 5.4, 14.9) with azithromycin compared to placebo among children recommended for treatment. For day 90 re

SUBMITTER: Kim SS 

PROVIDER: S-EPMC11581062 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature