Objective: The majority of detailed patient information in real-world data (RWD) is only consistently available in free-text clinical documents. Manual curation is expensive and time-consuming. Developing natural language processing (NLP) methods for structuring RWD is thus essential for scaling real-world evidence generation. Materials and Methods: Traditional rule-based systems are vulnerable to the prevalent linguistic variations and ambiguities in clinical text, and prior applications of machine-learning methods typically require sentence-level or report-level labeled examples that are hard to produce at scale. We propose leveraging patient-level supervision from medical registries, which are often readily available and capture key patient information, for general RWD applications. To combat the lack of sentence-level or report-level annotations, we explore advanced deep-learning methods by combining domain-specific pretraining, recurrent neural networks, and hierarchical attention. Results: We conduct an extensive study on 135,107 patients from the cancer registry of a large integrated delivery network (IDN) comprising healthcare systems in five western US states. Our deep learning methods attain test AUROC of 94-99% for key tumor attributes and comparable performance on held-out data from separate health systems and states. Discussion and Conclusion: Ablation results demonstrate clear superiority of these advanced deep-learning methods over prior approaches. Error analysis shows that our NLP system sometimes even corrects errors in registrar labels. We also conduct a preliminary investigation in accelerating registry curation and general RWD structuring via assisted curation for over 1.2 million cancer patients in this healthcare network.

本文介绍了使用自然语言处理技术和基于深度学习的方法处理医学记录中的实际数据，通过利用医学注册处重点患者信息的导向监督，并结合领域特定的预训练、递归神经网络和分层注意的方法，实现了在医疗系统中具有可伸缩性的实证研究。

Towards Structuring Real-World Data at Scale: Deep Learning for
  Extracting Key Oncology Information from Clinical Text with Patient-Level
  Supervision

大规模结构化现实世界数据的研究: 利用患者级监督的深度学习从临床文本中提取关键肿瘤信息