Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports.

ABSTRACT:

Importance

Clinical text reports from head computed tomography (CT) represent rich, incompletely utilized information regarding acute brain injuries and neurologic outcomes. CT reports are unstructured; thus, extracting information at scale requires automated natural language processing (NLP). However, designing new NLP algorithms for each individual injury category is an unwieldy proposition. An NLP tool that summarizes all injuries in head CT reports would facilitate exploration of large data sets for clinical significance of neuroradiological findings.

Objective

To automatically extract acute brain pathological data and their features from head CT reports.

Design, setting, and participants

This diagnostic study developed a 2-part named entity recognition (NER) NLP

SUBMITTER: Torres-Lopez VM 

PROVIDER: S-EPMC9382443 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature