Automating Stroke Data Extraction From Free-Text Radiology Reports Using Natural Language Processing: Instrument Validation Study.

ABSTRACT:

Background

Diagnostic neurovascular imaging data are important in stroke research, but obtaining these data typically requires laborious manual chart reviews.

Objective

We aimed to determine the accuracy of a natural language processing (NLP) approach to extract information on the presence and location of vascular occlusions as well as other stroke-related attributes based on free-text reports.

Methods

From the full reports of 1320 consecutive computed tomography (CT), CT angiography, and CT perfusion scans of the head and neck performed at a tertiary stroke center between October 2017 and January 2019, we manually extracted data on the presence of proximal large vessel occlusion (primary outcome), as well as distal vessel occlusion, ischemia, hemorrhage, Alberta stro

SUBMITTER: Yu AYX 

PROVIDER: S-EPMC8132979 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature