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RadLex Normalization in Radiology Reports.

Surabhi Datta1, Jordan Godfrey-Stovall1, Kirk Roberts1

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This summary is machine-generated.

This study introduces novel natural language processing (NLP) methods to standardize radiology report entities using RadLex. The best model achieved 78.44% accuracy in normalizing radiological terms.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Radiology

Background:

  • Radiology reports contain vital patient imaging data.
  • Standardizing radiological entities to a common vocabulary is under-researched.
  • No prior studies have utilized RadLex for this standardization.

Purpose of the Study:

  • To normalize diverse radiological entities to RadLex terms.
  • To develop and evaluate deep learning NLP models for automatic normalization.

Main Methods:

  • Manual construction of a normalization corpus with 1706 annotated entities from three report types.
  • Implementation of two BERT-based NLP models for entity normalization.
  • Utilizing BM25 for candidate concept retrieval to aid BERT models.

Main Results:

  • The best performing model, a BERT-based span detector, achieved 78.44% accuracy in normalizing radiological entities.
  • The study demonstrates the feasibility of deep learning for RadLex normalization.

Conclusions:

  • Deep learning NLP models show promise for standardizing radiology report entities to RadLex.
  • Challenges in corpus construction were identified, and new RadLex terms were proposed.