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Background: In the information extraction and natural language processingdomain, accessible datasets are crucial to reproduce and compare results.Publicly available implementations and tools can serve as benchmark andfacilitate the development of more complex applications. However, in thecontext of clinical text processing the number of accessible datasets is scarce-- and so is the number of existing tools. One of the main reasons is thesensitivity of the data. This problem is even more evident for non-Englishlanguages.  Approach: In order to address this situation, we introduce a workbench: acollection of German clinical text processing models. The models are trained ona de-identified corpus of German nephrology reports.  Result: The presented models provide promising results on in-domain data.Moreover, we show that our models can be also successfully applied to otherbiomedical text in German. Our workbench is made publicly available so it canbe used out of the box, as a benchmark or transferred to related problems.

A Medical Information Extraction Workbench to Process German Clinical Text