Before revision: 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 .
After revision: 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 medical concepts with cross-lingual support. We train CODER via contrastive learning on a medical knowledge graph with multi-language support .