Keywords: transparent models, missing data, robust methods, earth mover’s distance, predicting intensive care unit outcomes, random forests, support vector machines
We explore ways to predict mortality in critical care situations, such as ICUs. Current models use a small number of variables, no temporal features, and are regression based with manual variable selection and weighting. We develop a univariate flagging algorithm (UFA) that predicts well, scales to a large number of variables, is robust to missing data, and easy to interpret and visualize. While Random Forests, etc. can be competitive with UFA in these situations, they are a black box to the practitioners using them.