  Electronic health records (EHR) consist of longitudinal clinical observations
portrayed with sparsity, irregularity, and high-dimensionality, which become
major obstacles in drawing reliable downstream clinical outcomes. Although
there exist great numbers of imputation methods to tackle these issues, most of
them ignore correlated features, temporal dynamics and entirely set aside the
uncertainty. Since the missing value estimates involve the risk of being
inaccurate, it is appropriate for the method to handle the less certain
information differently than the reliable data. In that regard, we can use the
uncertainties in estimating the missing values as the fidelity score to be
further utilized to alleviate the risk of biased missing value estimates. In
this work, we propose a novel variational-recurrent imputation network, which
unifies an imputation and a prediction network by taking into account the
correlated features, temporal dynamics, as well as the uncertainty.
Specifically, we leverage the deep generative model in the imputation, which is
based on the distribution among variables, and a recurrent imputation network
to exploit the temporal relations, in conjunction with utilization of the
uncertainty. We validated the effectiveness of our proposed model on two
publicly available real-world EHR datasets: PhysioNet Challenge 2012 and
MIMIC-III, and compared the results with other competing state-of-the-art
methods in the literature.

Machine Learning;Machine Learning