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Coder Eng
CODER is a knowledge-enhanced cross-language medical term embedding model, focusing on the task of medical terminology standardization.
Downloads 4,298
Release Time : 3/2/2022
Model Overview
By integrating knowledge graph embedding and contrastive learning techniques, CODER achieves cross-language medical term representation and standardization, aiming to address the standardization issues of medical terms across different languages and contexts.
Model Features
Knowledge Enhancement
Incorporates knowledge graph embedding techniques to enhance the semantic representation of medical terms
Cross-Language Support
Capable of handling medical terminology standardization tasks in multiple languages
Contrastive Learning
Uses contrastive learning methods to optimize term representation and improve standardization effectiveness
Model Capabilities
Medical Term Representation
Cross-Language Term Standardization
Medical Term Matching
Use Cases
Healthcare Information Standardization
Electronic Medical Record Term Standardization
Standardizes medical terms from different sources in electronic medical records into unified terminology
Improves interoperability and analysis efficiency of healthcare data
Cross-Language Medical Term Mapping
Establishes accurate correspondences between medical terms in different languages
Facilitates international healthcare data exchange and sharing
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