  In recent years, we have witnessed an increased interest in temporal modeling
of patient records from large scale Electronic Health Records (EHR). While
simpler RNN models have been used for such problems, memory networks, which in
other domains were found to generalize well, are underutilized. Traditional
memory networks involve diffused and non-linear operations where influence of
past events on outputs are not readily quantifiable. We posit that this lack of
interpretability makes such networks not applicable for EHR analysis. While
networks with explicit memory have been proposed recently, the discontinuities
imposed by the discrete operations make such networks harder to train and
require more supervision. The problem is further exacerbated in the limited
data setting of EHR studies. In this paper, we propose a novel memory
architecture that is more interpretable than traditional memory networks while
being easier to train than explicit memory banks. Inspired by well-known models
of human cognition, we propose partitioning the external memory space into (a)
a primary explicit memory block to store exact replicas of recent events to
support interpretations, followed by (b) a secondary blurred memory block that
accumulates salient aspects of past events dropped from the explicit block as
higher level abstractions and allow training with less supervision by stabilize
the gradients. We apply the model for 3 learning problems on ICU records from
the MIMIC III database spanning millions of data points. Our model performs
comparably to the state-of the art while also, crucially, enabling ready
interpretation of the results.
