论文标题
迈向自动化解血摘要：基于BERT症状的模型
Towards Automated Anamnesis Summarization: BERT-based Models for Symptom Extraction
论文作者
论文摘要
现代医疗保健系统中的专业人员越来越受文档工作量负担。初始患者解血的文献特别相关，构成了成功的进一步诊断措施的基础。但是，手动准备的笔记本质上是非结构化的，而且通常不完整。在本文中，我们研究了现代NLP技术在此问题上支持医生的潜力。我们介绍了德国患者独白的数据集，并在现实世界实用程序和实用性的限制下制定了明确的信息提取任务。此外，我们建议基于BERT的模型来解决上述任务。我们可以在症状识别和症状属性提取中表现出有希望的表现，从而明显优于简单的基准。
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.
