  Machine learning models that utilize patient data across time (rather than
just the most recent measurements) have increased performance for many risk
stratification tasks in the intensive care unit. However, many of these models
and their learned representations are complex and therefore difficult for
clinicians to interpret, creating challenges for validation. Our work proposes
a new procedure to learn summaries of clinical time-series that are both
predictive and easily understood by humans. Specifically, our summaries consist
of simple and intuitive functions of clinical data (e.g. falling mean arterial
pressure). Our learned summaries outperform traditional interpretable model
classes and achieve performance comparable to state-of-the-art deep learning
models on an in-hospital mortality classification task.
