  With COVID-19 now pervasive, identification of high-risk individuals is
crucial. Using data from a major healthcare provider in Southwestern
Pennsylvania, we develop survival models predicting severe COVID-19
progression. In this endeavor, we face a tradeoff between more accurate models
relying on many features and less accurate models relying on a few features
aligned with clinician intuition. Complicating matters, many EHR features tend
to be under-coded, degrading the accuracy of smaller models. In this study, we
develop two sets of high-performance risk scores: (i) an unconstrained model
built from all available features; and (ii) a pipeline that learns a small set
of clinical concepts before training a risk predictor. Learned concepts boost
performance over the corresponding features (C-index 0.858 vs. 0.844) and
demonstrate improvements over (i) when evaluated out-of-sample (subsequent time
periods). Our models outperform previous works (C-index 0.844-0.872 vs.
0.598-0.810).
