We propose a novel medical term embedding method named CODER, which stands for mediCal knOwledge embeDded tErm Representation . CODER is designed for medical term normalization by providing close vector representations for  terms that represent the same or similar concepts with multi-language support. CODER is trained on top of BERT (Devlin et al., 2018) with the innovation that token vector aggregation is trained using relations from the UMLS Metathesaurus (Bodenreider, 2004), which is a comprehensive medical knowledge graph with multi-language support . Training with relations injects medical knowledge into term embeddings and aims to provide better normalization performances and potentially better machine learning features. We evaluated CODER in term normalization, semantic similarity, and relation classification benchmarks, which showed that CODER outperformed various state-of-the-art biomedical word embeddings , concept embeddings, and contextual embeddings. 
This paper proposes CODER: contrastive learning on knowledge graphs for cross-lingual medical term representation . CODER is designed for medical term normalization by providing close vector representations for different terms that represent the same or similar medical concepts with cross-lingual support. We train CODER via contrastive learning on a medical knowledge graph (KG) named the Unified Medical Language System, where similarities are calculated utilizing both terms and relation triplets from KG . Training with relations injects medical knowledge into embeddings and aims to provide potentially better machine learning features. We evaluate CODER in zero-shot term normalization, semantic similarity, and relation classification benchmarks, which show that CODERoutperforms various state-of-the-art biomedical word embedding , concept embeddings, and contextual embeddings. Our codes and models are available at URL
R
We propose a novel medical term embedding method named CODER, which stands for mediCal knOwledge embeDded tErm Representation
This paper proposes CODER: contrastive learning on knowledge graphs for cross-lingual medical term representation
A
different
R
concepts with multi-language support. CODER is trained on top of BERT (Devlin et al., 2018) with the innovation that token vector aggregation is trained using relations from the UMLS Metathesaurus (Bodenreider, 2004), which is a comprehensive
medical concepts with cross-lingual support. We train CODER via contrastive learning on a
R
with multi-language support
(KG) named the Unified Medical Language System, where similarities are calculated utilizing both terms and relation triplets from KG
D
term
D
better normalization performances and
R
evaluated CODER in
evaluate CODER in zero-shot
R
showed that CODER outperformed
show that CODERoutperforms
R
embeddings
embedding
A
Our codes and models are available at URL