Fig. 3

Clinical decision tree (DT). A clinical DT model predicting the discharge disposition of a patient (survival or death) was developed. A The tree shows the rules applied to classify each patient into the related classes (survival or death). At the top of the DT, the overall proportion of the patients survived (95%) or died (5%) is shown. Next, the node applies the threshold over clinical data to achieve classification of patients into the two classes. For instance, it applies the threshold of 2.7 g/dL over Albumin_24_hours_min (minimum value obtained from the clinical data), the node evaluates whether if patients show Albumin_24_hours_min above 2.7. If yes, then the next decision rule in DT is at down to the root’s left child node (Yes; depth 2). Ninety-one percent of patients will survive with a survival probability of ninety-nine percent. This way, inspecting the whole DT, the impact of features on the likelihood of survival can be derived. The percentage of patients at each node is provided below the probability values of survival (denoted as 1) or death (denoted as 2) on the DT; the green (survived) /blue (died) shows the fitted/estimated values for the patients in each class at given node. ROC curves for B training set and C test set. AUC provides an aggregate measure of performance across all possible classification thresholds