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.

翻译：现代医疗体系的专业人员日益受到文件工作量的沉重负担,最初的病人厌食症的文献特别相关,是进一步诊断措施取得成功的基础。然而,人工编写的笔记本质上是没有结构的,而且往往不完整。在本文中,我们调查了现代NLP技术在这方面支持医生的潜力。我们提供了一套德国病人独白的数据集,在现实世界的实用性和实用性的限制下,制定了定义明确的信息提取任务。此外,我们提出了基于BERT的模型,以解决上述任务。我们可以证明这些模型在症状识别和症状属性提取两方面都具有良好的表现,明显优于较简单的基线。