Figure 5.
Artificial intelligence–derived predictors of outcomes in patients. (A) There were no statistically significant differences in the distribution of the median levofloxacin 0–24 hour area under the concentration-time curve (AUC0-24) by treatment outcome using standard statistical inferences. (B) Ranking of important variables from 2 random forest models based on 2 definitions of outcome revealed the effect of drug concentration on outcomes. (C) Representative classification and regression tree used to generate the random forest model output shows a threshold AUC0-24/minimum inhibitory concentration of 160. Once the threshold exposure has been identified using machine learning, it can be used in standard statistics to show an odds of failure below this threshold, which in this case shows a higher odds ratio of failure with a 95% confidence interval of 1.15 to infinity (given the 0% failure rate above the threshold value). Abbreviations: AUC0-24, 0–24 hour area under the concentration-time curve; BMI, body mass index; MIC, minimum inhibitory concentration; ROCL, receiver operating characteristic Learn set; ROCT, receiver operating characteristic Test set; TC, treatment complete.