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Extraction of UMLS® Concepts Using Apache cTAKES™ for German Language
Matthias Becker1, Britta Böckmann1
1Department of Medical Informatics, University of Applied Sciences and Arts, Dortmund, Germany.
Studies in Health Technology and Informatics
|May 4, 2016
Summary
This study adapted Apache cTAKES for German clinical notes to extract Unified Medical Language System (UMLS) concepts. The natural language processing pipeline achieved an average F1 score of 0.36 for concept extraction and mapping.
Area of Science:
- Medical Informatics
- Natural Language Processing
- Computational Linguistics
Background:
- Standardizing medical information from clinical reports is crucial for clinical research.
- Automated extraction and classification of medical concepts are essential for this standardization.
Purpose of the Study:
- To evaluate the suitability of Apache cTAKES for German clinical notes.
- To test the extraction and mapping of Unified Medical Language System (UMLS) concepts to SNOMED-CT using a customized natural language processing pipeline.
Main Methods:
- A natural language processing pipeline was customized using German UMLS data and German OpenNLP models.
- Apache cTAKES was adapted for German clinical notes.
- The system was tested on a German translation of the ShARe/CLEF eHealth 2013 training dataset (199 reports).
Main Results:
- The implemented algorithms achieved an average F1 measure of 0.36.
- This result was obtained without German stemming, pre-processing, or post-processing.
Conclusions:
- The customized natural language processing pipeline shows potential for UMLS concept extraction from German clinical text.
- Further optimization, including stemming and pre/post-processing, may improve performance.
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