Accepted for/Published in: JMIR Formative Research
Date Submitted: Apr 27, 2022
Date Accepted: Nov 3, 2022
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
GERNERMED: An Open German Medical NER Model
ABSTRACT
Background:
Data mining in the field of medical data analysis often needs to rely solely on processing of unstructured data to retrieve relevant data. For German NLP, no open medical neural named entity recognition (NER) model has been published prior to this work. A major issue can be attributed to the lack of German training data.
Objective:
We develop a novel German medical NER model for public access. In order to bypass legal restrictions due to potential data leaks through model analysis, we do not make use of internal, proprietary datasets.
Methods:
The underlying German dataset is retrieved by translation and word alignment of a public English dataset. The dataset serves as foundation for model training and evaluation.
Results:
The obtained dataset consists of 8599 sentences including 30233 annotations. The model achieves an averaged f1 score of 0.82 on the test set after training across seven different NER types. The model is publicly available.
Conclusions:
We demonstrate the feasibility of training a German medical NER model by the exclusive use of public training data. The sample code and the statistical model are available on GitHub.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.