Named entity recognition for Chinese electronic medical records by integrating knowledge graph and ClinicalBERT.

ABSTRACT:

Introduction

General purpose language models often struggle with accurately identifying domain specific terminology in the medical field, resulting in suboptimal performance in named entity recognition (NER) tasks. This challenge is particularly pronounced in Chinese electronic medical records (EMRs), which lack clear word boundaries and contain complex medical expressions.

Methods

This study proposes a novel NER method for Chinese EMRs that integrates ClinicalBERT, a language model pre trained on clinical corpora, with structured knowledge from a medical knowledge graph. Entity representations derived via Translating Embeddings (TransE) are incorporated to inject external semantic knowledge. Furthermore, the model fuses multiple character level features, including positiona

SUBMITTER: Xu X 

PROVIDER: S-EPMC12457354 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature