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Related Concept Videos

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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Medical named entity recognition based on domain knowledge and position encoding.

Shuifa Sun1, Qin Hu1, Fengjiao Xu2

  • 1School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.

BMC Medical Informatics and Decision Making
|July 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel model for Chinese electronic medical record named entity recognition, improving accuracy by integrating domain knowledge and advanced positional encoding. The model enhances boundary detection for better clinical data analysis.

Keywords:
BERTMedical domain dictionaryNamed entity recognitionRoPEStar-Transformer

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Area of Science:

  • Natural Language Processing
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Accurate named entity recognition (NER) in Chinese electronic medical records (EMRs) is crucial for clinical data analysis.
  • Existing models often struggle with precise boundary detection and incorporating domain-specific medical terminology.

Purpose of the Study:

  • To develop a novel NER model for Chinese EMRs with enhanced boundary detection capabilities.
  • To leverage medical domain knowledge and advanced positional encoding techniques to improve recognition accuracy.

Main Methods:

  • A BERT module integrated with a lexical adapter for medical domain-specific terms.
  • Feature extraction using Star-Transformer for local features and BiLSTM for long-distance features.
  • Incorporation of Rotary Position Embedding (RoPE) to enhance semantic feature extraction.

Main Results:

  • Achieved an F1-score of 85.78% on the CCKS2020 dataset, a 2.96% increase over the baseline.
  • Demonstrated improved performance on a self-built breast cancer ultrasound report dataset.
  • Validated the model's effectiveness and applicability in the medical field.

Conclusions:

  • The proposed model significantly improves named entity recognition in Chinese EMRs.
  • The integration of domain knowledge, advanced encoding, and specific architectures enhances boundary detection and overall accuracy.
  • The model shows strong potential for real-world applications in processing clinical text data.