  Making the most use of abundant information in electronic health records
(EHR) is rapidly becoming an important topic in the medical domain. Recent work
presented a promising framework that embeds entire features in raw EHR data
regardless of its form and medical code standards. The framework, however, only
focuses on encoding EHR with minimal preprocessing and fails to consider how to
learn efficient EHR representation in terms of computation and memory usage. In
this paper, we search for a versatile encoder not only reducing the large data
into a manageable size but also well preserving the core information of
patients to perform diverse clinical tasks. We found that hierarchically
structured Convolutional Neural Network (CNN) often outperforms the
state-of-the-art model on diverse tasks such as reconstruction, prediction, and
generation, even with fewer parameters and less training time. Moreover, it
turns out that making use of the inherent hierarchy of EHR data can boost the
performance of any kind of backbone models and clinical tasks performed.
Through extensive experiments, we present concrete evidence to generalize our
research findings into real-world practice. We give a clear guideline on
building the encoder based on the research findings captured while exploring
numerous settings.
