Using the Shapes of Clinical Data Trajectories to Predict Mortality in ICUs

We show that statistical learning techniques can be used to automatically extract all relevant shape features for use in predictive modeling. The approach requires no additional data and can potentially be used to improve any risk model that uses some form of trajectory information. In this single-center study, the shapes of the clinical data trajectories convey information about ICU mortality risk beyond what is already captured by the summary statistics currently used in the literature.