论文标题
在放射学报告中利用空间信息进行缺血性中风表型
Leveraging Spatial Information in Radiology Reports for Ischemic Stroke Phenotyping
论文作者
论文摘要
对细粒性缺血性中风表型进行分类依赖于确定重要的临床信息。放射学报告提供了有关上下文的相关信息，以确定此类表型信息。我们专注于带有特定于位置的信息的中风表型：受到大脑区域的影响，横向性，中风阶段和骨质性。我们使用现有的细粒空间信息提取系统（RAD-SpatialNet） - 识别临床上重要的信息，并在提取的信息上应用简单的域规则以对表型进行分类。我们提出的方法的性能是有希望的（对大脑区域进行分类为89.62％，将大脑区域，侧面和中风阶段分类为74.11％）。我们的工作表明，可以利用基于细粒模式的信息提取系统来确定复杂的表型，其中包括简单的域规则。这些表型具有促进基于中风位置的势后结果和治疗计划的中风研究。
Classifying fine-grained ischemic stroke phenotypes relies on identifying important clinical information. Radiology reports provide relevant information with context to determine such phenotype information. We focus on stroke phenotypes with location-specific information: brain region affected, laterality, stroke stage, and lacunarity. We use an existing fine-grained spatial information extraction system--Rad-SpatialNet--to identify clinically important information and apply simple domain rules on the extracted information to classify phenotypes. The performance of our proposed approach is promising (recall of 89.62% for classifying brain region and 74.11% for classifying brain region, side, and stroke stage together). Our work demonstrates that an information extraction system based on a fine-grained schema can be utilized to determine complex phenotypes with the inclusion of simple domain rules. These phenotypes have the potential to facilitate stroke research focusing on post-stroke outcome and treatment planning based on the stroke location.
