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Temporal bone radiology report classification using open source machine learning and natural langue processing

Aaron J Masino1, Robert W Grundmeier2,3, Jeffrey W Pennington2

  • 1Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, 3535 Market Street, Suite 1024, Philadelphia, PA, 19104, USA. masinoa@email.chop.edu.

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Summary
This summary is machine-generated.

Machine learning models accurately classify radiology reports for ear abnormalities, improving the Audiological and Genetic Database (AudGenDB). This enables faster identification of specific patient cohorts for research.

Keywords:
AudiologyHuman-in-the-loopMachine learningNatural language processingRadiology

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Area of Science:

  • Biomedical informatics
  • Medical imaging analysis
  • Machine learning in healthcare

Background:

  • Radiology reports are valuable for research but require manual review for data extraction.
  • The Audiological and Genetic Database (AudGenDB) contains over 16,000 de-identified radiology reports.
  • Manual labeling of reports for specific abnormalities is time-consuming and difficult.

Purpose of the Study:

  • To develop and implement a machine learning pipeline for automated labeling of radiology reports.
  • To identify specific ear abnormalities (inner, middle, outer, mastoid) within radiology reports.
  • To enhance cohort identification within the AudGenDB for biomedical research.

Main Methods:

  • A human-in-the-loop approach was used with open-source libraries.
  • Radiology reports were converted into n-gram feature vectors.
  • Several classification models (logistic regression, SVM, decision tree, random forest, Naïve Bayes) were trained and evaluated.

Main Results:

  • The best classifiers achieved high accuracy: 90% (inner ear), 90% (middle ear), 93% (outer ear), and 82% (mastoid).
  • Logistic regression demonstrated consistency, with accuracy scores close to the best classifiers.
  • Receiver operating characteristic (ROC) area under the curve (AUC) was 0.92 or greater across all regions.

Conclusions:

  • The developed machine learning models achieve sufficient accuracy for extracting discrete features from radiology reports.
  • The open-source models and web service facilitate accessibility and utilization for research.
  • Automated labeling enhances cohort identification in the AudGenDB, streamlining research processes.