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Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model.

Wilson Lau1, Kevin Lybarger2, Martin L Gunn3

  • 1Biomedical & Health Informatics, School of Medicine, University of Washington, Seattle, WA, USA. wlau@uw.edu.

Journal of Digital Imaging
|October 17, 2022
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Summary
This summary is machine-generated.

We developed a deep learning model to extract clinical findings from radiology reports, achieving high accuracy. This enables better data utilization for diagnosis, triage, and research.

Keywords:
Deep learningEvent extractionInformation extractionNatural language processing

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Area of Science:

  • Natural Language Processing
  • Medical Informatics
  • Artificial Intelligence in Radiology

Background:

  • Radiology reports contain valuable clinical information.
  • Extracting this data semantically supports secondary applications like diagnosis and research.
  • Existing methods lack comprehensive representations of radiological findings.

Purpose of the Study:

  • To develop and validate a deep learning model for extracting detailed clinical findings from radiology reports.
  • To create a new corpus of annotated radiology reports for training and evaluation.
  • To demonstrate the model's generalizability across institutions and imaging modalities.

Main Methods:

  • Developed an event-based annotation schema for clinical findings (lesions, medical problems).
  • Utilized BERT-based deep learning architectures for entity and relation extraction.
  • Trained and validated models on a corpus of 500 annotated computed tomography (CT) reports.
  • Evaluated model performance on an external dataset from the MIMIC Chest X-ray (MIMIC-CXR) database.

Main Results:

  • Achieved high F1 scores for finding trigger extraction (90.9-93.4%) and argument role prediction (72.0-85.6%) on the internal CT dataset.
  • Demonstrated strong generalizability with F1 scores of 95.6% for triggers and 79.1-89.7% for argument roles on the MIMIC-CXR dataset.
  • Successfully extracted finding events from the entire MIMIC-CXR database.

Conclusions:

  • The developed deep learning model effectively extracts detailed clinical findings from radiology reports.
  • The model shows excellent performance and generalizability across different institutions and imaging modalities.
  • This work facilitates secondary use of radiology report data for clinical decision support and research.