A Probabilistic Model of Cardiac Physiology and Electrocardiograms
This work addresses the challenge of improving diagnostic accuracy in healthcare by providing a physiological representation of heart function, though it appears incremental as it builds on existing modeling approaches.
The authors tackled the problem of modeling electrocardiograms (EKGs) by developing a probabilistic model based on cardiac physiology, which more accurately reconstructed missing data in EKG reports compared to a standard baseline, particularly when data was significantly missing.
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.