  Electronic health record (EHR) systems are used extensively throughout the
healthcare domain. However, data interchangeability between EHR systems is
limited due to the use of different coding standards across systems. Existing
methods of mapping coding standards based on manual human experts mapping,
dictionary mapping, symbolic NLP and classification are unscalable and cannot
accommodate large scale EHR datasets.
  In this work, we present Text2Node, a cross-domain mapping system capable of
mapping medical phrases to concepts in a large taxonomy (such as SNOMED CT).
The system is designed to generalize from a limited set of training samples and
map phrases to elements of the taxonomy that are not covered by training data.
As a result, our system is scalable, robust to wording variants between coding
systems and can output highly relevant concepts when no exact concept exists in
the target taxonomy. Text2Node operates in three main stages: first, the
lexicon is mapped to word embeddings; second, the taxonomy is vectorized using
node embeddings; and finally, the mapping function is trained to connect the
two embedding spaces. We compared multiple algorithms and architectures for
each stage of the training, including GloVe and FastText word embeddings, CNN
and Bi-LSTM mapping functions, and node2vec for node embeddings. We confirmed
the robustness and generalisation properties of Text2Node by mapping ICD-9-CM
Diagnosis phrases to SNOMED CT and by zero-shot training at comparable
accuracy.
  This system is a novel methodological contribution to the task of normalizing
and linking phrases to a taxonomy, advancing data interchangeability in
healthcare. When applied, the system can use electronic health records to
generate an embedding that incorporates taxonomical medical knowledge to
improve clinical predictive models.
