Predicting Acute Cerebrovascular Events in Stroke Alerts Using Large-Language Models and Structured Data.

ABSTRACT:

Background

Acute stroke alerts are often activated for non-cerebrovascular conditions, leading to false positives that strain clinical resources and promote diagnostic uncertainty. We sought to develop machine learning (ML) models integrating large-language models (LLMs), structured electronic health record data, and clinical time series data to predict the presence of acute cerebrovascular disease (ACD) at stroke alert activation.

Methods

We derived a series of ML models using retrospective data from stroke alerts activated at Mount Sinai Health System between 2011 and 2021. We extracted structured data (demographics, medical comorbidities, medications, and engineered time-series features from vital signs and lab results) as well as unstructured clinical notes available pri

SUBMITTER: Erekat AN 

PROVIDER: S-EPMC12622098 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature