In the first part of the talk 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.
In the second part we consider methods to quantify potential uncertainty in plots and images.The basic idea is to find a way to remove structure from the image, bootstrap what is left, and then restore the structure leading to, say, and 1000 images.The Earth Mover’s Distance allows us to compute the distances between these plots and optimization algorithms allow us to order the plots and then find, say the lower extreme, middle, and upper extreme to visualize the uncertainty that may be present in the plot or image.