Integrating structured and unstructured data for predicting emergency severity: an association and predictive study using transformer-based natural language processing models.

ABSTRACT:

Background

Efficient triage in emergency departments (EDs) is critical for timely and appropriate care. Traditional triage systems primarily rely on structured data, but the increasing availability of unstructured data, such as clinical notes, presents an opportunity to enhance predictive models for assessing emergency severity and to explore associations between patient characteristics and severity outcomes. This study aimed to evaluate the effectiveness of combining structured and unstructured data to predict emergency severity more accurately.

Methods

Data from the 2021 National Hospital Ambulatory Medical Care Survey (NHAMCS) for adult ED patients were used. Emergency severity was categorized into urgent (scores 1-3) and non-urgent (scores 4-5) based on the Emergency Sev

SUBMITTER: Zhang X 

PROVIDER: S-EPMC11619330 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature