  Real-world Electronic Health Records (EHRs) are often plagued by a high rate
of missing data. In our EHRs, for example, the missing rates can be as high as
90% for some features, with an average missing rate of around 70% across all
features. We propose a Time-Aware Dual-Cross-Visit missing value imputation
method, named TA-DualCV, which spontaneously leverages multivariate
dependencies across features and longitudinal dependencies both within- and
cross-visit to maximize the information extracted from limited observable
records in EHRs. Specifically, TA-DualCV captures the latent structure of
missing patterns across measurements of different features and it also
considers the time continuity and capture the latent temporal missing patterns
based on both time-steps and irregular time-intervals. TA-DualCV is evaluated
using three large real-world EHRs on two types of tasks: an unsupervised
imputation task by varying mask rates up to 90% and a supervised 24-hour early
prediction of septic shock using Long Short-Term Memory (LSTM). Our results
show that TA-DualCV performs significantly better than all of the existing
state-of-the-art imputation baselines, such as DETROIT and TAME, on both types
of tasks.
