Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, 19104, USA.
J Digit Imaging. 2021 Apr;34(2):374-384. doi: 10.1007/s10278-021-00423-8. Epub 2021 Feb 10.
Recommendations are a key component of radiology reports. Automatic extraction of recommendations would facilitate tasks such as recommendation tracking, quality improvement, and large-scale descriptive studies. Existing report-parsing systems are frequently limited to recommendations for follow-up imaging studies, operate at the sentence or document level rather than the individual recommendation level, and do not extract important contextualizing information. We present a neural network architecture capable of extracting fully contextualized recommendations from any type of radiology report. We identified six major "questions" necessary to capture the majority of context associated with a recommendation: recommendation, time period, reason, conditionality, strength, and negation. We developed a unified task representation by allowing questions to refer to answers to other questions. Our representation allows for a single system to perform named entity recognition (NER) and classification tasks. We annotated 2272 radiology reports from all specialties, imaging modalities, and multiple hospitals across our institution. We evaluated the performance of a long short-term memory (LSTM) architecture on the six-question task. The single-task LSTM model achieves a token-level performance of 89.2% at recommendation extraction, and token-level performances between 85 and 95% F1 on extracting modifying features. Our model extracts all types of recommendations, including follow-up imaging, tissue biopsies, and clinical correlation, and can operate in real time. It is feasible to extract complete contextualized recommendations of all types from arbitrary radiology reports. The approach is likely generalizable to other clinical entities referenced in radiology reports, such as radiologic findings or diagnoses.
推荐意见是放射学报告的一个重要组成部分。自动提取推荐意见将有助于推荐意见跟踪、质量改进和大规模描述性研究等任务。现有的报告解析系统通常仅限于随访影像学研究的推荐意见，在句子或文档级别而不是单个推荐级别上运行，并且不提取重要的上下文信息。我们提出了一种能够从任何类型的放射学报告中提取完全上下文化推荐意见的神经网络架构。我们确定了六个主要的“问题”，这些问题对于捕获与推荐意见相关的大多数上下文信息是必要的：推荐意见、时间段、原因、条件、强度和否定。我们通过允许问题引用其他问题的答案来开发统一的任务表示。我们的表示允许单个系统执行命名实体识别 (NER) 和分类任务。我们对来自我们机构的所有专业、成像方式和多个医院的 2272 份放射学报告进行了注释。我们在六个问题任务上评估了长短期记忆 (LSTM) 架构的性能。单个任务 LSTM 模型在推荐提取方面的令牌级性能达到 89.2%，在提取修改特征方面的令牌级性能在 85%至 95% F1 之间。我们的模型可以提取所有类型的推荐意见，包括随访影像学、组织活检和临床相关性，并且可以实时运行。从任意放射学报告中提取所有类型的完整上下文化推荐意见是可行的。这种方法很可能适用于放射学报告中引用的其他临床实体，例如放射学发现或诊断。
