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Published on: February 23, 2019
Semantic structuring of and information extraction from medical documents using the UMLS
1Research Center L3S, Appelstrasse 9a, 30167 Hanover, Germany. denecke@L3S.de
Methods of Information in Medicine
|October 15, 2008
Summary
SeReMeD automatically generates knowledge representations from medical documents, achieving high accuracy in extracting diagnoses and procedures. This method simplifies medical information extraction and allows for adaptable domain specialization.
Area of Science:
- Natural Language Processing
- Medical Informatics
- Knowledge Representation
Background:
- Automated knowledge representation from medical documents is crucial for data analysis.
- Existing methods require significant effort and domain-specific customization.
Purpose of the Study:
- Introduce SeReMeD (Semantic Representation of Medical Documents) for automated knowledge extraction.
- Analyze the suitability of the Unified Medical Language System (UMLS) for this task.
Main Methods:
- SeReMeD combines language engineering with semantic rules to map text to structured data.
- It extracts concepts and semantic roles from medical narratives.
- Tested on chest X-ray reports for data extraction.
Main Results:
- Semantic structure generation achieved 80% correctness.
- Extraction of documented findings showed 93% precision and 83% recall.
- The system demonstrated effectiveness on real-world medical reports.
Conclusions:
- SeReMeD accurately extracts data from medical narratives, with potential for further improvement.
- It reduces development effort by leveraging existing NLP techniques.
- The system's domain knowledge is adaptable for various medical sub-domains.
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