  Medical data sets are usually corrupted by noise and missing data. These
missing patterns are commonly assumed to be completely random, but in medical
scenarios, the reality is that these patterns occur in bursts due to sensors
that are off for some time or data collected in a misaligned uneven fashion,
among other causes. This paper proposes to model medical data records with
heterogeneous data types and bursty missing data using sequential variational
autoencoders (VAEs). In particular, we propose a new methodology, the Shi-VAE,
which extends the capabilities of VAEs to sequential streams of data with
missing observations. We compare our model against state-of-the-art solutions
in an intensive care unit database (ICU) and a dataset of passive human
monitoring. Furthermore, we find that standard error metrics such as RMSE are
not conclusive enough to assess temporal models and include in our analysis the
cross-correlation between the ground truth and the imputed signal. We show that
Shi-VAE achieves the best performance in terms of using both metrics, with
lower computational complexity than the GP-VAE model, which is the
state-of-the-art method for medical records.
