  While the volume of electronic health records (EHR) data continues to grow,
it remains rare for hospital systems to capture dense physiological data
streams, even in the data-rich intensive care unit setting. Instead, typical
EHR records consist of sparse and irregularly observed multivariate time
series, which are well understood to present particularly challenging problems
for machine learning methods. In this paper, we present a new deep learning
architecture for addressing this problem based on the use of a semi-parametric
interpolation network followed by the application of a prediction network. The
interpolation network allows for information to be shared across multiple
dimensions during the interpolation stage, while any standard deep learning
model can be used for the prediction network. We investigate the performance of
this architecture on the problems of mortality and length of stay prediction.
