Figure 2

Triaged Prediction Approach. The goal of the triaged prediction approach was to use the most minimal set of data possible to predict a given patient’s outcome and identify potential patient sub-groups based off of prediction patterns. At each epoch of analysis, a classifier was trained and predictions were made using 10-fold cross-validation. Subsequently, patients were classified as either having a mild, severe disease and/or death, or indeterminate 14-day outcome. Patients who achieved an outcome during the epoch were removed from further analysis. After prediction, Kullback-Leibler (KL) divergence was used to identify features that distinguished true positive from false negative predictions and the proportion of errors in each unsupervised cluster were calculated.