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Related Experiment Video

Updated: Jan 1, 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

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Terminologies augmented recurrent neural network model for clinical named entity recognition.

Ivan Lerner1, Nicolas Paris2, Xavier Tannier3

  • 1Paris University, Paris, France; AP-HP, DSI-WIND, Paris, France.

Journal of Biomedical Informatics
|December 15, 2019
PubMed
Summary

This study enhanced clinical named-entity recognition (NER) performance by integrating medical terminologies into supervised models. The hybrid system achieved high accuracy on the French APcNER corpus, demonstrating improved clinical text analysis.

Keywords:
APcNERClinical natural language processingInformation extractionMachine learningNamed entity recognition

Related Experiment Videos

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Medical Informatics

Background:

  • Clinical Named-Entity Recognition (NER) is crucial for extracting information from medical texts.
  • Existing supervised models can be improved by incorporating medical terminologies.
  • A French corpus for clinical NER was needed for evaluation.

Purpose of the Study:

  • To enhance supervised clinical NER models using medical terminologies.
  • To evaluate system performance in French using a newly created corpus.
  • To compare different NER system architectures.

Main Methods:

  • Developed a terminology-based system using UMLS and SNOMED as a baseline.
  • Evaluated a biGRU-CRF model and a hybrid system combining terminology predictions with biGRU-CRF.
  • Created the APcNER corpus in French, annotated for 5 clinical entity types.

Main Results:

  • The biGRU-CRF outperformed the terminology-based system for drug name recognition.
  • The hybrid system achieved higher performance than the biGRU-CRF on both English and French corpora.
  • On the APcNER corpus, the hybrid system reached an exact-match F-measure of 69.5% and a non-exact match F-measure of 84.1%.

Conclusions:

  • The APcNER corpus is a valuable resource for French clinical NER.
  • Integrating terminologies into supervised models significantly improves NER performance, especially for rare entities.
  • The developed hybrid system achieved near state-of-the-art results.