  In this paper, we present a new deep learning architecture for addressing the
problem of supervised learning with sparse and irregularly sampled multivariate
time series. The architecture is 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 of a multivariate time series during the interpolation stage, while
any standard deep learning model can be used for the prediction network. This
work is motivated by the analysis of physiological time series data in
electronic health records, which are sparse, irregularly sampled, and
multivariate. We investigate the performance of this architecture on both
classification and regression tasks, showing that our approach outperforms a
range of baseline and recently proposed models.
