Leveraging Spatial Information in Radiology Reports for Ischemic Stroke Phenotyping

TL;DR

This paper presents a method that leverages spatial information extraction from radiology reports to classify ischemic stroke phenotypes, aiding research and treatment planning.

Contribution

It introduces the use of Rad-SpatialNet combined with domain rules to classify complex stroke phenotypes from radiology reports.

Findings

Recall of 89.62% for brain region classification

Recall of 74.11% for combined phenotype classification

Demonstrates the utility of schema-based information extraction for clinical phenotyping

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.