  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.
