Leveraging Spatial Information in Radiology Reports for Ischemic Stroke Phenotyping
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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 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.
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