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Evaluating Report Text Variation and Informativeness: Natural Language Processing of CT Chest Imaging for Pulmonary

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Area of Science:

  • Radiology and Medical Imaging
  • Natural Language Processing
  • Machine Learning in Healthcare

Background:

  • Free text reports are common in radiology, but their variability can impact interpretation.
  • Pulmonary embolus (PE) diagnosis relies on accurate interpretation of imaging reports.

Purpose of the Study:

  • To quantify language variability in free text pulmonary embolus (PE) reports.
  • To assess the informativeness of free text for predicting PE diagnosis using machine learning.

Main Methods:

  • Analysis of 1,133 consecutive chest CTs with contrast for PE protocol.
  • Utilized text-mining and predictive analytics software for machine learning rule generation.

Main Results:

  • Extensive variation in report length and terms used; limited association with PE diagnosis.
  • Machine learning achieved perfect sensitivity but imperfect specificity (73% PPV, 3% misclassification).

Conclusions:

  • Free text reporting leads to significant variability and interpretation challenges.
  • Structured report templates may improve report usability and understanding for PE diagnosis.