Using Large Language Models to Extract Core Injury Information From Emergency Department Notes.

ABSTRACT:

Background

Injuries pose a significant global health challenge due to their high incidence and mortality rates. Although injury surveillance is essential for prevention, it is resource-intensive. This study aimed to develop and validate locally deployable large language models (LLMs) to extract core injury-related information from Emergency Department (ED) clinical notes.

Methods

We conducted a diagnostic study using retrospectively collected data from January 2014 to December 2020 from two urban academic tertiary hospitals. One served as the derivation cohort and the other as the external test cohort. Adult patients presenting to the ED with injury-related complaints were included. Primary outcomes included classification accuracies for information extraction tasks related

SUBMITTER: Choi DH 

PROVIDER: S-EPMC11611659 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature