Accuracy of Large Language Models to Identify Stroke Subtypes Within Unstructured Electronic Health Record Data.

ABSTRACT:

Background

While International Classification of Diseases, Tenth Revision codes suffice for identifying stroke events in surveillance, accurately classifying stroke types and subtypes using electronic health records remains challenging due to limitations in structured data. This often necessitates manual review of clinical documentation. This study evaluated whether a large language model, Generative Pre-Trained Transformer 4 Omni (GPT-4o), can accurately identify stroke types and subtypes from unstructured clinical notes.

Methods

We implemented a retrieval-augmented generation framework with GPT-4o to classify stroke types (ischemic versus hemorrhagic) and ischemic stroke subtypes using electronic health records data. The American Heart Association Get With The Guide

SUBMITTER: Owens D 

PROVIDER: S-EPMC12313299 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature