Electronic health records (EHR) are widely believed to hold a profusion of
actionable insights, encrypted in an irregular, semi-structured format, amidst
a loud noise background. To simplify learning patterns of health and disease,
medical codes in EHR can be decomposed into semantic units connected by
hierarchical graphs. Building on earlier synergy between Bidirectional Encoder
Representations from Transformers (BERT) and Graph Attention Networks (GAT), we
present H-BERT, which ingests complete graph tree expansions of hierarchical
medical codes as opposed to only ingesting the leaves and pushes patient-level
labels down to each visit. This methodology significantly improves prediction
of patient membership in over 500 medical diagnosis classes as measured by
aggregated AUC and APS, and creates distinct representations of patients in
closely related but clinically distinct phenotypes.Comment: Extended Abstract presented at Machine Learning for Health (ML4H)
symposium 2022, November 28th, 2022, New Orleans, United States & Virtual,
http://www.ml4h.cc, 9 page