We define a representation framework for extracting spatial information from radiology reports (Rad-SpRL). We annotated a total of 2000 chest X-ray reports with 4 spatial roles corresponding to the common radiology entities. Our focus is on extracting detailed information of a radiologist's interpretation containing a radiographic finding, its anatomical location, corresponding probable diagnoses, as well as associated hedging terms. For this, we propose a deep learning-based natural language processing (NLP) method involving both word and character-level encodings. Specifically, we utilize a bidirectional long short-term memory (Bi-LSTM) conditional random field (CRF) model for extracting the spatial roles. The model achieved average F1 measures of 90.28 and 94.61 for extracting the Trajector and Landmark roles respectively whereas the performance was moderate for Diagnosis and Hedge roles with average F1 of 71.47 and 73.27 respectively. The corpus will soon be made available upon request.

该研究提出了一种基于深度学习自然语言处理的方法，通过从放射学报告中提取空间信息，包括放射学发现、解剖位置、可能诊断以及相关避免使用术语等，从而为诊断提供详细信息。

Understanding Spatial Language in Radiology: Representation Framework,
  Annotation, and Spatial Relation Extraction from Chest X-ray Reports using
  Deep Learning

深度学习在胸部 X-Ray 报告中理解空间语言的表示框架，标注和空间关系提取