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Rule-based natural language processing for automation of stroke data extraction: a validation study.

Dane Gunter1, Paulo Puac-Polanco2, Olivier Miguel2

  • 1The Ottawa Hospital Research Institute, Ottawa, ON, Canada.

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|August 1, 2022
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Summary
This summary is machine-generated.

This study validates CHARTextract, a rule-based natural language processing (NLP) algorithm, for extracting stroke data from radiology reports. The NLP algorithm shows moderate to good performance, with accuracy over 90% for several key stroke indicators.

Keywords:
Data extractionNatural language processingRule-basedStrokeStroke surveillance

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Area of Science:

  • Medical Informatics
  • Radiology
  • Natural Language Processing

Background:

  • Manual data extraction from radiology reports is labor-intensive.
  • Natural Language Processing (NLP) offers automated solutions for data extraction.
  • A prior rule-based NLP algorithm demonstrated potential for stroke data extraction.

Purpose of the Study:

  • To externally validate the accuracy of CHARTextract, a rule-based NLP algorithm.
  • To assess the algorithm's ability to extract stroke-related data from free-text radiology reports.

Main Methods:

  • Analysis of CT angiography (CTA) and perfusion (CTP) reports from acute ischemic stroke patients (2015-2021).
  • Manual extraction of stroke variables (occlusion, ischemia, hemorrhage, ASPECTS, collaterals) as the reference standard.
  • Simultaneous data extraction using the CHARTextract rule-based NLP algorithm.
  • Assessment of algorithm performance using accuracy, sensitivity, specificity, PPV, and NPV.

Main Results:

  • Accuracy exceeded 90% for distal anterior occlusion, posterior circulation occlusion, hemorrhage, and ASPECTS.
  • Accuracy ranged from 74% to 85% for proximal anterior circulation occlusion, ischemia, and collateral status.
  • The algorithm achieved 87-100% accuracy in confirming the absence of reported variables.

Conclusions:

  • Rule-based NLP demonstrates moderate to good performance in extracting stroke data from radiology reports.
  • Algorithm accuracy is influenced by variations in radiologist reporting styles and terminology.
  • External validation confirms the utility of NLP for streamlining stroke data extraction.