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Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

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Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
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Developing a RadLex-Based Named Entity Recognition Tool for Mining Textual Radiology Reports: Development and

Shintaro Tsuji1,2, Andrew Wen1, Naoki Takahashi3

  • 1Department of Health Sciences Research, Department of Radiology, Rochester, MN, United States.

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|October 29, 2021
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Summary
This summary is machine-generated.

This study developed a RadLex-based tool to improve named entity recognition (NER) in radiology reports, enhancing the extraction of complex medical terms. The new approach significantly boosted performance by creating an enhanced dictionary and analyzing stem terms.

Keywords:
RadLexnamed entity recognition (NER)natural language processing (NLP)ontologystem term

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Radiology

Background:

  • Named Entity Recognition (NER) is crucial for extracting disease features from free-text radiology reports.
  • Existing NER tools struggle with compound terms due to dictionary limitations and varied patterns.

Purpose of the Study:

  • To develop and evaluate a novel NER tool for compound terms in radiology reports using RadLex.
  • To enhance the mining of free-text radiology reports by improving NER accuracy for complex terms.

Main Methods:

  • Utilized clinical Text Analysis and Knowledge Extraction System (cTAKES) with RadLex and SentiWordNet.
  • Manually annotated 400 radiology reports for compound terms, creating a gold standard.
  • Developed a compound terms-enhanced dictionary (CtED) and analyzed stem term measures (OR, MR) on 100 reports.

Main Results:

  • The combined CtED achieved a 63.1% F-measure, significantly improving precision (82.8%) and recall (51%).
  • RadLex enhanced the matching ratio (MR) by approximately 22% compared to the default dictionary.
  • Stem term analysis indicated potential for generating synonymous phrases using ontologies.

Conclusions:

  • A RadLex-based customized pipeline was developed for parsing radiology reports.
  • CtED and stem term analysis show potential to improve dictionary-based NER by expanding vocabularies.