  Utilizing clinical texts in survival analysis is difficult because they are
largely unstructured. Current automatic extraction models fail to capture
textual information comprehensively since their labels are limited in scope.
Furthermore, they typically require a large amount of data and high-quality
expert annotations for training. In this work, we present a novel method of
using BERT-based hidden layer representations of clinical texts as covariates
for proportional hazards models to predict patient survival outcomes. We show
that hidden layers yield notably more accurate predictions than predefined
features, outperforming the previous baseline model by 5.7% on average across
C-index and time-dependent AUC. We make our work publicly available at
https://github.com/bionlplab/heart_failure_mortality.
