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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Automating Stroke Data Extraction From Free-Text Radiology Reports Using Natural Language Processing: Instrument

Amy Y X Yu1, Zhongyu A Liu1, Chloe Pou-Prom2

  • 1Department of Medicine (Neurology), University of Toronto - Sunnybrook Health Sciences Centre, Toronto, ON, Canada.

JMIR Medical Informatics
|May 4, 2021
PubMed
Summary
This summary is machine-generated.

This study shows natural language processing (NLP) accurately extracts stroke imaging data from reports, improving efficiency for large-scale stroke research and surveillance. The NLP approach demonstrated high accuracy in identifying large vessel occlusions.

Keywords:
data extractiondiagnostic imagingimagingnatural language processingneurovascularstrokestroke surveillancesurveillance

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Area of Science:

  • Medical Imaging
  • Natural Language Processing
  • Stroke Research

Background:

  • Diagnostic neurovascular imaging data are crucial for stroke research.
  • Manual chart reviews for data extraction are time-consuming and laborious.

Purpose of the Study:

  • To evaluate the accuracy of a natural language processing (NLP) approach for extracting stroke-related information from free-text reports.
  • To assess NLP's ability to identify vascular occlusions and other stroke indicators.

Main Methods:

  • A rule-based NLP model was developed and trained on 921 reports.
  • The NLP model's performance was validated against manually extracted data from 399 reports.
  • Key outcomes included presence of large vessel occlusion, ischemia, hemorrhage, ASPECTS, and collateral status.

Main Results:

  • The NLP approach achieved high overall accuracy in identifying large vessel occlusions (97.3% in training, 95.2% in validation).
  • High accuracy was also observed for distal occlusions and hemorrhage.
  • Limitations were noted in extracting data on cerebral ischemia, ASPECTS, and collateral status.

Conclusions:

  • Natural language processing (NLP) offers a promising method to enhance the efficiency of collecting imaging data for stroke research.
  • This approach can facilitate large-scale stroke surveillance and data analysis.