  An electrocardiogram (EKG) is a common, non-invasive test that measures the
electrical activity of a patient's heart. EKGs contain useful diagnostic
information about patient health that may be absent from other electronic
health record (EHR) data. As multi-dimensional waveforms, they could be modeled
using generic machine learning tools, such as a linear factor model or a
variational autoencoder. We take a different approach:~we specify a model that
directly represents the underlying electrophysiology of the heart and the EKG
measurement process. We apply our model to two datasets, including a sample of
emergency department EKG reports with missing data. We show that our model can
more accurately reconstruct missing data (measured by test reconstruction
error) than a standard baseline when there is significant missing data. More
broadly, this physiological representation of heart function may be useful in a
variety of settings, including prediction, causal analysis, and discovery.
