Unified Clinical Vocabulary Embeddings for Advancing Precision Medicine.

ABSTRACT: Integrating clinical knowledge into AI remains challenging despite numerous medical guidelines and vocabularies. Medical codes, central to healthcare systems, often reflect operational patterns shaped by geographic factors, national policies, insurance frameworks, and physician practices rather than the precise representation of clinical knowledge. This disconnect hampers AI in representing clinical relationships, raising concerns about bias, transparency, and generalizability. Here, we developed a resource of 67,124 clinical vocabulary embeddings derived from a clinical knowledge graph tailored to electronic health record vocabularies, spanning over 1.3 million edges. Using graph transformer neural networks, we generated clinical vocabulary embeddings that provide a new representation of

SUBMITTER: Johnson R 

PROVIDER: S-EPMC11643188 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature