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Understanding spatial language in radiology: Representation framework, annotation, and spatial relation extraction

Surabhi Datta1, Yuqi Si1, Laritza Rodriguez2

  • 1School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, United States.

Journal of Biomedical Informatics
|June 21, 2020
PubMed
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This study introduces Rad-SpRL, a framework for extracting spatial information from radiology reports. Deep learning models achieved high accuracy in identifying anatomical locations and associated diagnoses, improving clinical informatics.

Area of Science:

  • Natural Language Processing (NLP)
  • Medical Informatics
  • Radiology

Background:

  • Radiology reports describe spatial relationships crucial for diagnosis.
  • Extracting this spatial information is challenging but vital for clinical applications.

Purpose of the Study:

  • To develop a method for extracting spatial representations from radiology reports.
  • To define a framework (Rad-SpRL) for encoding spatial information.

Main Methods:

  • Annotated 2,000 chest X-ray reports using the Rad-SpRL framework.
  • Employed deep learning NLP models (Bi-LSTM-CRF, BERT, XLNet) for information extraction.
  • Utilized word and character-level encodings for spatial indicator and role identification.

Main Results:

Keywords:
Deep learningNLPRadiology reportSpatial relations

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  • Achieved high F1 scores for Spatial Indicator extraction (up to 91.29% with XLNet).
  • Obtained strong overall F1 measures for spatial role extraction (92.9% with gold indicators, 85.6% with predicted indicators using BERT).

Conclusions:

  • The proposed Rad-SpRL framework and deep learning methods effectively extract clinically significant spatial information from radiology reports.
  • This work facilitates downstream clinical informatics applications by structuring unstructured text data.