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Related Experiment Video

Updated: May 31, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Large language model augmented framework with domain-specific knowledge integration for medical named entity

Haochen Zou1, Yongli Wang1

  • 1School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei Street No. 200, Nanjing, 210094, Jiangsu, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 29, 2026
PubMed
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A new framework enhances medical named entity recognition (NER) by integrating domain-specific knowledge with large language models (LLMs). This approach improves accuracy and consistency in identifying medical terms from diverse texts.

Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Medical named entity recognition (NER) is crucial for downstream applications but faces challenges like domain specialization and data standardization.
  • Existing large language models (LLMs) and NER frameworks struggle with the nuances of specialized medical knowledge and resource accessibility.

Purpose of the Study:

  • To propose a novel LLM-augmented framework for medical NER that integrates domain-specific knowledge.
  • To address limitations in current medical NER approaches, including domain adaptation and knowledge resource integration.

Main Methods:

  • A framework decomposing medical NER into recognition, refinement, and entity type identification using a medical LLM and a task-adaptive recognizer.
  • Iterative refinement and standardization via knowledge augmentation to incorporate domain-specific knowledge and ensure consistency.
Keywords:
Health informaticsKnowledge augmentationKnowledge enhancementLarge language modelNamed entity recognition

Related Experiment Videos

Last Updated: May 31, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

  • An entity type identifier utilizing unified contextual information for effective type determination.

    • Main Results:

    • The framework was evaluated on over 26,000 samples across three benchmark datasets, covering 16 entity types.
    • Achieved average evaluation metrics of 78.24%, 95.89%, and 96.08% on the benchmark datasets.
    • Outperformed 12 baseline methods by approximately 3%, demonstrating enhanced performance in medical NER.

    Conclusions:

    • The proposed framework effectively integrates domain-specific knowledge for progressive medical NER.
    • This innovative approach offers a robust solution for knowledge-intensive tasks in the medical domain.