Abstract
Learning representations of medical concepts from
the Electronic Health Record (EHR) has been
shown effective for predictive analytics in healthcare. Incorporation of medical ontologies has also
been explored to further enhance the accuracy and
to ensure better alignment with the known medical
knowledge. Most of the existing works assume that
medical concepts under the same ontological category should share similar representations, which
however does not always hold. In particular, the
categorizations in medical ontologies were established with various factors being considered. Medical concepts even under the same ontological category may not follow similar occurrence patterns
in the EHR data, leading to contradicting objectives for the representation learning. In this paper,
we propose a deep learning model called MMORE
which alleviates this conflicting objective issue by
allowing multiple representations to be inferred for
each ontological category via an attention mechanism. We apply MMORE to diagnosis prediction
and our experimental results show that the representations obtained by MMORE can achieve better
predictive accuracy and result in clinically meaningful sub-categorizations of the existing ontological categories