Natural Language Processing of Radiology Reports to Detect Complications of Ischemic Stroke.

ABSTRACT:

Background

Abstraction of critical data from unstructured radiologic reports using natural language processing (NLP) is a powerful tool to automate the detection of important clinical features and enhance research efforts. We present a set of NLP approaches to identify critical findings in patients with acute ischemic stroke from radiology reports of computed tomography (CT) and magnetic resonance imaging (MRI).

Methods

We trained machine learning classifiers to identify categorical outcomes of edema, midline shift (MLS), hemorrhagic transformation, and parenchymal hematoma, as well as rule-based systems (RBS) to identify intraventricular hemorrhage (IVH) and continuous MLS measurements within CT/MRI reports. Using a derivation cohort of 2289 reports from 550 individuals wit

SUBMITTER: Miller MI 

PROVIDER: S-EPMC9986939 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature