  Machine learning has recently demonstrated impressive progress in predictive
accuracy across a wide array of tasks. Most ML approaches focus on
generalization performance on unseen data that are similar to the training data
(In-Distribution, or IND). However, real world applications and deployments of
ML rarely enjoy the comfort of encountering examples that are always IND. In
such situations, most ML models commonly display erratic behavior on
Out-of-Distribution (OOD) examples, such as assigning high confidence to wrong
predictions, or vice-versa. Implications of such unusual model behavior are
further exacerbated in the healthcare setting, where patient health can
potentially be put at risk. It is crucial to study the behavior and robustness
properties of models under distributional shift, understand common failure
modes, and take mitigation steps before the model is deployed. Having a
benchmark that shines light upon these aspects of a model is a first and
necessary step in addressing the issue. Recent work and interest in increasing
model robustness in OOD settings have focused more on image modality, while the
Electronic Health Record (EHR) modality is still largely under-explored. We aim
to bridge this gap by releasing BEDS-Bench, a benchmark for quantifying the
behavior of ML models over EHR data under OOD settings. We use two open access,
de-identified EHR datasets to construct several OOD data settings to run tests
on, and measure relevant metrics that characterize crucial aspects of a model's
OOD behavior. We evaluate several learning algorithms under BEDS-Bench and find
that all of them show poor generalization performance under distributional
shift in general. Our results highlight the need and the potential to improve
robustness of EHR models under distributional shift, and BEDS-Bench provides
one way to measure progress towards that goal.
