  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.
