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arXiv · 2010.05096

Leveraging Spatial Information in Radiology Reports for Ischemic Stroke Phenotyping

Surabhi Datta·Shekhar Khanpara·Roy F. Riascos·Kirk Roberts
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Abstract

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.

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Surabhi Datta, Shekhar Khanpara, Roy F. Riascos, Kirk Roberts. 2020-10-10. Leveraging Spatial Information in Radiology Reports for Ischemic Stroke Phenotyping. https://arxiv.org/abs/2010.05096

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