  Professionals in modern healthcare systems are increasingly burdened by
documentation workloads. Documentation of the initial patient anamnesis is
particularly relevant, forming the basis of successful further diagnostic
measures. However, manually prepared notes are inherently unstructured and
often incomplete. In this paper, we investigate the potential of modern NLP
techniques to support doctors in this matter. We present a dataset of German
patient monologues, and formulate a well-defined information extraction task
under the constraints of real-world utility and practicality. In addition, we
propose BERT-based models in order to solve said task. We can demonstrate
promising performance of the models in both symptom identification and symptom
attribute extraction, significantly outperforming simpler baselines.
