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

Survival-Supervised Topic Modeling with Anchor Words: Characterizing Pancreatitis Outcomes