  We introduce a new approach for topic modeling that is supervised by survival
analysis. Specifically, we build on recent work on unsupervised topic modeling
with so-called anchor words by providing supervision through an elastic-net
regularized Cox proportional hazards model. In short, an anchor word being
present in a document provides strong indication that the document is partially
about a specific topic. For example, by seeing "gallstones" in a document, we
are fairly certain that the document is partially about medicine. Our proposed
method alternates between learning a topic model and learning a survival model
to find a local minimum of a block convex optimization problem. We apply our
proposed approach to predicting how long patients with pancreatitis admitted to
an intensive care unit (ICU) will stay in the ICU. Our approach is as accurate
as the best of a variety of baselines while being more interpretable than any
of the baselines.
