Information Extraction Models for German Clinical Text

Roland Roller, Laura Seiffe, Ammer Ayach, Sebastian Möller, Oliver Marten, Michael Mikhailov, Christoph Alt, Danilo Schmidt, Fabian Halleck, Marcel Naik, Wiebke Duettmann, Klemens Budde

Abstract: Tools and resources to automatically process clinical text are very limited, particularly outside the English speaking world. As many relevant patient information within electronic health records are described in unstructured text, this is a clear drawback. In order to slightly overcome this problem, we present information extraction models for German clinical text and make them freely available. The models have been trained on documents of the nephrology domain and do not contain personal information.