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Related Experiment Video

Updated: Dec 2, 2025

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COVID-19 detection in radiological text reports integrating entity recognition.

Pilar López-Úbeda1, Manuel Carlos Díaz-Galiano1, Teodoro Martín-Noguerol2

  • 1SINAI Group, CEATIC, Universidad de Jaén, Campus Las Lagunillas S/N, E-23071, Jaén, Spain.

Computers in Biology and Medicine
|November 1, 2020
PubMed
Summary

This study developed an automated system using Natural Language Processing (NLP) to detect COVID-19 radiological findings in chest CT reports. The system achieved 90% accuracy, aiding clinicians in diagnosing COVID-19 lung involvement.

Keywords:
COVID-19Named entity recognitionNatural language processingRadiological reportText classification

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Area of Science:

  • Medical Imaging Analysis
  • Natural Language Processing
  • Computational Medicine

Background:

  • Chest Computed Tomography (CT) and PCR tests are standard for COVID-19 diagnosis, but radiological reports contain vital diagnostic information.
  • Automated systems using Natural Language Processing (NLP) can support clinicians by identifying COVID-19 related disorders in radiological reports.

Purpose of the Study:

  • To develop and evaluate a text classification system for automatically predicting COVID-19 radiological findings from chest CT reports.
  • To enhance diagnostic support for clinicians by leveraging NLP techniques on textual radiological data.

Main Methods:

  • Utilized Machine Learning approaches and Named Entity Recognition on 295 chest CT radiological reports.
  • Integrated textual report data with COVID-19 related disorders from SNOMED-CT.
  • Employed Support Vector Machine (SVM) for classification and mutual information for data integration.

Main Results:

  • The baseline text classification system achieved 85% accuracy in predicting lung involvement consistent with COVID-19.
  • Integrating information sources, including SNOMED-CT entities, improved accuracy to approximately 90%.

Conclusions:

  • The developed NLP system effectively predicts COVID-19 radiological findings from chest CT reports.
  • The system demonstrates significant potential to assist in the early and accurate detection of COVID-19 lung involvement, improving clinical decision-making.