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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Automated Radiology Report Summarization Using an Open-Source Natural Language Processing Pipeline.

Daniel J Goff1, Thomas W Loehfelm2

  • 1Department of Radiology, University of California Davis Health System, 4860 Y Street, Suite 3100, Sacramento, CA, 95817, USA.

Journal of Digital Imaging
|November 1, 2017
PubMed
Summary

Automated natural language processing can extract disease mentions from radiology reports, improving efficiency and accuracy in clinical decision-making for diagnostic radiologists.

Keywords:
Data extractionNLPRadiology reportReport summarization

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Radiology

Background:

  • Radiologists review prior studies for comprehensive patient assessments.
  • Current methods for accessing prior studies are time-consuming and risk overlooking critical findings.
  • The volume of prior imaging studies can be substantial, increasing complexity.

Purpose of the Study:

  • To evaluate the feasibility of using natural language processing (NLP) for automated extraction of disease entities from free-text radiology reports.
  • To develop an automated report summarization pipeline as a step towards improving radiologist workflow.

Main Methods:

  • A natural language processing pipeline was developed to extract asserted and negated disease mentions.
  • The pipeline's performance was compared against a gold-standard set of manual annotations.
  • 50 free-text radiology reports from CT abdomen and pelvis examinations were analyzed.

Main Results:

  • The automated pipeline achieved a sensitivity of 0.86, precision of 0.66, and F1 score of 0.74.
  • 86% of manually annotated disease mentions had perfect or overlapping partial matches with automated extraction.
  • The overall accuracy of the automated system was comparable to interobserver agreement between manual annotators.

Conclusions:

  • Natural language processing shows promise for automatically extracting disease entities from radiology reports.
  • Automated summarization of radiology reports is a feasible next step.
  • NLP techniques can enhance diagnostic workflows by improving efficiency and potentially reducing overlooked findings.