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Supervised and unsupervised language modelling in Chest X-Ray radiological reports.

Ignat Drozdov1, Daniel Forbes2, Benjamin Szubert1

  • 1Bering Limited, London, United Kingdom.

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Deep neural networks (DNNs) can triage chest radiography (CXR) reports. Bi-directional long short-term memory (BiLSTM) networks with attention achieved high accuracy on normal and abnormal CXR reports, even with limited training data.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Chest radiography (CXR) is a primary imaging tool.
  • Deep neural networks (DNNs) show potential for CXR triage.
  • Labeling large datasets for DNNs is a significant clinical bottleneck.

Purpose of the Study:

  • To evaluate supervised classifiers for CXR report triage.
  • To develop an unsupervised approach for CXR report classification.
  • To facilitate automated clinical information extraction from CXR reports.

Main Methods:

  • Evaluated thirteen supervised classifiers on free-text CXR reports.
  • Utilized bi-directional long short-term memory (BiLSTM) networks with attention mechanism.
  • Developed a general unsupervised approach for normal vs. abnormal CXR report distinction.

Main Results:

  • BiLSTM networks achieved high f1-scores (0.94 internal, 0.90 external) with limited labeled data.
  • The unsupervised approach accurately distinguished normal and abnormal CXR reports in a large unlabeled corpus.
  • Demonstrated effective identification of Normal, Abnormal, and Unclear CXR reports.

Conclusions:

  • BiLSTM networks offer an effective solution for CXR report triage.
  • Unsupervised methods can aid in classifying CXR reports without extensive labeling.
  • The findings support the development of clinical decision support systems for CXR triage.