Exploiting hierarchy in medical concept embedding

Objective To construct and publicly release a set of medical concept
embeddings for codes following the ICD-10 coding standard which explicitly
incorporate hierarchical information from medical codes into the embedding
formulation. Materials and Methods We trained concept embeddings using
several new extensions to the Word2Vec algorithm using a dataset of
approximately 600,000 patients from a major integrated healthcare
organization in the Mid-Atlantic US. Our concept embeddings included
additional entities to account for the medical categories assigned to
codes by the Clinical Classification Software Revised (CCSR) dataset. We
compare these results to sets of publicly-released pretrained embeddings
and alternative training methodologies. Results We found that Word2Vec
models which included hierarchical data outperformed ordinary Word2Vec
alternatives on tasks which compared naïve clusters to canonical ones
provided by CCSR. Our Skip-Gram model with both codes and categories
achieved 61.4% Normalized Mutual Information with canonical labels in
comparison to 57.5% with traditional Skip-Gram. In models operating on two
different outcomes we found that including hierarchical embedding data
improved classification performance 96.2% of the time. When controlling
for all other variables, we found that co-training embeddings improved
classification performance 66.7% of the time. We found that all models
outperformed our competitive benchmarks. Discussion We found significant
evidence that our proposed algorithms can express the hierarchical
structure of medical codes more fully than ordinary Word2Vec models, and
that this improvement carries forward into classification tasks. As part
of this publication, we have released several sets of pretrained medical
concept embeddings using the ICD-10 standard which significantly
outperform other well-known pretrained vectors on our tested outcomes.