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Automatic extraction of concepts to extend RadLex.
Rebecca Hazen1, Alex P Van Esbroeck, Pat Mongkolwat
1Rochester Institute of Technology, Rochester, NY, USA.
Journal of Digital Imaging
|September 15, 2010
Summary
Automated methods can extract new radiology terms from medical literature to enhance controlled vocabularies like RadLex. This approach aids in identifying imaging observations and their characteristics for improved radiology lexicon development.
Area of Science:
- Medical Informatics
- Radiology Terminology
Background:
- The Radiology Lexicon (RadLex) is a controlled vocabulary for radiology developed by the Radiological Society of North America.
- Existing lexicons have gaps, particularly in imaging observations and their characteristics.
Purpose of the Study:
- To develop and evaluate an automated system for extracting candidate terms related to imaging observations and characteristics from the medical literature.
- To identify terms for potential inclusion in standardized radiology lexicons.
Main Methods:
- Utilized a free, open-source software system integrating PubMed retrieval and Apache Unstructured Information Management Architecture.
- Employed a seven-step process to identify imaging observations and characteristics from 1,128 journal articles.
- Domain experts evaluated the top 100 extracted terms for precision.
Main Results:
- The system generated lists of 624 imaging observations and 444 imaging observation characteristics.
- Expert evaluation yielded a precision of 52% for imaging observations and 26% for imaging observation characteristics among the top 100 terms.
- Demonstrated the feasibility of automatic term extraction from the scientific literature.
Conclusions:
- Candidate terms for standardized lexicons can be automatically extracted from the peer-reviewed literature.
- Extracted terms require expert review and curation for lexicon inclusion.
- This automated approach supports the ongoing development and expansion of radiology terminologies.
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