  Modeling the time-series of high-dimensional, longitudinal data is important
for predicting patient disease progression. However, existing neural network
based approaches that learn representations of patient state, while very
flexible, are susceptible to overfitting. We propose a deep generative model
that makes use of a novel attention-based neural architecture inspired by the
physics of how treatments affect disease state. The result is a scalable and
accurate model of high-dimensional patient biomarkers as they vary over time.
Our proposed model yields significant improvements in generalization and, on
real-world clinical data, provides interpretable insights into the dynamics of
cancer progression.
