Syndromic Analysis of Sepsis Cohorts Using Large Language Models.

ABSTRACT:

Importance

Presenting signs and symptoms affect the care of patients with possible sepsis. However, signs and symptoms are not incorporated into most large observational studies because they are difficult to extract from clinical notes at scale.

Objective

To assess the use of large language models (LLMs) to extract presenting signs and symptoms from admission notes and characterize their associations with infectious diagnoses, multidrug-resistant infections, and mortality.

Design, setting, and participants

This retrospective cohort study obtained data from 5 Massachusetts hospitals within 1 health care system between June 1, 2015, and August 1, 2022. Participants were hospitalized adult patients with possible infection (determined by blood culture drawn and intravenou

SUBMITTER: Pak TR 

PROVIDER: S-EPMC12552932 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature