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Annotation and initial evaluation of a large annotated German oncological corpus
Madeleine Kittner1, Mario Lamping2,3, Damian T Rieke2,3,4
1Knowledge Management for Bioinformatics, Humboldt Universität zu Berlin, Berlin, Germany.
JAMIA Open
|April 26, 2021
Summary
The Berlin-Tübingen-Oncology corpus (BRONCO) offers a new, open resource for German medical text research. This corpus aids in developing better information extraction tools for cancer patient data.
Area of Science:
- Natural Language Processing
- Medical Informatics
- Computational Linguistics
Background:
- Information Extraction (IE) from medical texts is crucial for research.
- Existing German medical corpora are limited, hindering reproducible research.
- Oncological discharge summaries contain valuable patient data.
Purpose of the Study:
- Introduce the Berlin-Tübingen-Oncology corpus (BRONCO), a novel, freely available resource.
- Facilitate reproducible research in Information Extraction from German medical texts.
- Provide a benchmark for developing and evaluating IE tools.
Main Methods:
- Compiled 200 deidentified German oncological discharge summaries.
- Annotated medical entities (diagnoses, treatments, medications) and attributes (negation, speculation).
- Ensured annotation quality through a structured, expert-led process.
Main Results:
- BRONCO contains 11,434 sentences and 89,942 tokens with 11,124 entity and 3,118 attribute annotations.
- 75% of the corpus is released for general use, with 25% reserved for unbiased evaluation.
- Baseline IE models achieved F1-scores of 0.72-0.90 for named entity recognition and 0.55 for negation detection.
Conclusions:
- BRONCO is the first sizable, freely available German medical corpus for IE research.
- Annotation of medical corpora is complex and time-consuming, underscoring the importance of resource sharing.
- Further research is needed to enhance IE performance for German medical texts to match English capabilities.
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