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Summary

Natural Language Processing (NLP) can significantly reduce manual review of chest radiograph reports for pneumonia validation. The ONYX system can replace nearly 90% of manual reviews with minimal misclassification.

Keywords:
Natural Language Processingpharmacoepidemiologypneumoniasensitivityspecificityvalidity

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Area of Science:

  • Medical Informatics
  • Radiology
  • Natural Language Processing

Background:

  • Manual validation of chest radiograph reports for pneumonia is time-consuming.
  • Automated methods are needed to improve efficiency in clinical research and practice.

Purpose of the Study:

  • To develop and validate Natural Language Processing (NLP) approaches for validating pneumonia cases from chest radiograph reports.
  • To assess the performance of an NLP system (ONYX) in reducing manual review workload.

Main Methods:

  • Trained the ONYX NLP system on manually reviewed chest radiograph reports from children and adults.
  • Assessed ONYX performance on a test set of 5000 reports, classifying them into "consistent with pneumonia," "inconsistent with pneumonia," or "requiring manual review."
  • Optimized ONYX for either accuracy or minimal manual review, modeling its sensitivity and specificity using logistic regression.

Main Results:

  • When tailored for accuracy, ONYX required manual review for 25% of reports, achieving 92% sensitivity and 87% specificity on the remaining reports.
  • When tailored to minimize manual review, ONYX required manual review for 12% of reports, achieving 75% sensitivity and 95% specificity on the remainder.
  • Positive Predictive Value (PPV) ranged from 74-86%, and Negative Predictive Value (NPV) ranged from 91-96% depending on the tailoring.

Conclusions:

  • The ONYX NLP system can effectively replace a substantial portion (nearly 90%) of manual review for pneumonia validation from chest radiograph reports.
  • The system's performance can be tailored to balance accuracy and the reduction of manual review, making it adaptable to different study needs.
  • ONYX shows promise for broader application in validating clinical outcomes from text-based reports in various settings.