  EHR systems lack a unified code system forrepresenting medical concepts,
which acts asa barrier for the deployment of deep learningmodels in large scale
to multiple clinics and hos-pitals. To overcome this problem, we
introduceDescription-based Embedding,DescEmb, a code-agnostic representation
learning framework forEHR. DescEmb takes advantage of the flexibil-ity of
neural language understanding models toembed clinical events using their
textual descrip-tions rather than directly mapping each event toa dedicated
embedding. DescEmb outperformedtraditional code-based embedding in
extensiveexperiments, especially in a zero-shot transfertask (one hospital to
another), and was able totrain a single unified model for heterogeneousEHR
datasets.
