Detecting Named Entities and Relations in German Clinical Reports

Roland Roller, Nils Rethmeier, Philippe Thomas, Marc Hübner, Hans Uszkoreit, Fabian Halleck, Oliver Staeck, Klemens Budde, Danilo Schmidt

In: Proceedings of the International Conference of the German Society for Computational Linguistics and Language Technology. Conference of the German Society for Computational Linguistics and Language Technology (GSCL-2017) September 13-14 Berlin Germany Springer 2017.


Clinical notes and discharge summaries are commonly used in the clinical routine and contain patient related information such as well-being, findings and treatments. Information is often described in text form and presented in a semi-structured way. This makes it difficult to access the highly valuable information for patient support or clinical studies. Information extraction can help clinicians to access this information. However, most methods in the clinical domain focus on English data. This work aims at information extraction from German nephrology reports. We present on-going work in the context of detecting named entities and relations. Underlying to this work is a currently generated corpus annotation which includes a large set of different medical concepts, attributes and relations. At the current stage we apply a number of classification techniques to the existing dataset and achieve promising results for most of the frequent concepts and relations.


Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence