Natural Language Processing for the Identification of Silent Brain Infarcts From Neuroimaging Reports.

ABSTRACT:

Background

Silent brain infarction (SBI) is defined as the presence of 1 or more brain lesions, presumed to be because of vascular occlusion, found by neuroimaging (magnetic resonance imaging or computed tomography) in patients without clinical manifestations of stroke. It is more common than stroke and can be detected in 20% of healthy elderly people. Early detection of SBI may mitigate the risk of stroke by offering preventative treatment plans. Natural language processing (NLP) techniques offer an opportunity to systematically identify SBI cases from electronic health records (EHRs) by extracting, normalizing, and classifying SBI-related incidental findings interpreted by radiologists from neuroimaging reports.

Objective

This study aimed to develop NLP systems to determin

SUBMITTER: Fu S 

PROVIDER: S-EPMC6524454 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature