Automated Extraction of Stroke Severity from Unstructured Electronic Health Records using Natural Language Processing.

ABSTRACT:

Background

Multi-center electronic health records (EHR) can support quality improvement initiatives and comparative effectiveness research in stroke care. However, limitations of EHR-based research include challenges in abstracting key clinical variables from non-structured data at scale. This is further compounded by missing data. Here we develop a natural language processing (NLP) model that automatically reads EHR notes to determine the NIH stroke scale (NIHSS) score of patients with acute stroke.

Methods

The study included notes from acute stroke patients (>= 18 years) admitted to the Massachusetts General Hospital (MGH) (2015-2022). The MGH data were divided into training (70%) and hold-out test (30%) sets. A two-stage model was developed to predict the admission NIHSS.

SUBMITTER: Fernandes M 

PROVIDER: S-EPMC10980121 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature