  The recent availability of large datasets in bio-medicine has inspired the
development of representation learning methods for multiple healthcare
applications. Despite advances in predictive performance, the clinical utility
of such methods is limited when exposed to real-world data. This study develops
model diagnostic measures to detect potential pitfalls before deployment
without assuming access to external data. Specifically, we focus on modeling
realistic data shifts in electrophysiological signals (EEGs) via data
transforms and extend the conventional task-based evaluations with analyses of
a) the model's latent space and b) predictive uncertainty under these
transforms. We conduct experiments on multiple EEG feature encoders and two
clinically relevant downstream tasks using publicly available large-scale
clinical EEGs. Within this experimental setting, our results suggest that
measures of latent space integrity and model uncertainty under the proposed
data shifts may help anticipate performance degradation during deployment.
