Modeling MRSA decolonization: Interactions between body sites and the impact of site-specific clearance

TL;DR

This paper presents a machine learning model trained on RCT data to understand interactions between body sites in MRSA decolonization, aiding in optimizing therapy protocols for better outcomes.

Contribution

The study introduces a novel machine learning approach that estimates interactions between body sites and predicts therapy efficacy in MRSA decolonization.

Findings

Model estimates interactions between body sites.

Quantifies contribution of each therapy.

Predicts efficacy of therapy combinations.

Abstract

MRSA colonization is a critical public health concern. Decolonization protocols have been designed for the clearance of MRSA. Successful decolonization protocols reduce disease incidence; however, multiple protocols exist, comprising diverse therapies targeting multiple body sites, and the optimal protocol is unclear. Here, we formulate a machine learning model using data from a randomized controlled trial (RCT) of MRSA decolonization, which estimates interactions between body sites, quantifies the contribution of each therapy to successful decolonization, and enables predictions of the efficacy of therapy combinations. This work shows how a machine learning model can help design and improve complex clinical protocols.