Figure 2.

Variable importance plot from the random forest model, displaying the 10 most important variables for predicting antimicrobial stewardship program (ASP) intervention, in descending order of importance. Importance is measured by the mean decrease in model accuracy, which is roughly analogous to the loss in classifier accuracy when a given variable is excluded (ie, more important predictors will cause greater decreases in model predictive accuracy when they are removed from consideration during model-building). Some predictors may be collinear or represent similar concepts. For example, both our antimicrobial classification schema^a and an alternative Duke/Centers for Disease Control and Prevention antimicrobial classification schema^b that we also provided to the model [12] both made it into the top 10 predictors list. This suggests that regardless of the exact classification schema used, antimicrobial class is an important variable for predicting which antimicrobial order reviews will result in ASP intervention. Abbreviations: ASP, antimicrobial stewardship program; CDC, Centers for Disease Control and Prevention; ID, infectious disease.