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ABSTRACT: Purpose
To evaluate the performance of a large language model (LLM) in classifying electronic health record (EHR) text, and to use this classification to evaluate the type and resolution of hemorrhagic events (HEs) after microinvasive glaucoma surgery (MIGS).Design
Retrospective cohort study.Participants
Eyes from the Bascom Palmer Glaucoma Repository.Methods
Eyes that underwent MIGS between July 1, 2014 and February 1, 2022 were analyzed. Chat Generative Pre-trained Transformer (ChatGPT) was used to classify deidentified EHR anterior chamber examination text into HE categories (no hyphema, microhyphema, clot, and hyphema). Agreement between classifications by ChatGPT and a glaucoma specialist was evaluated using Cohen's Kappa and precision-recall (PR) curve
SUBMITTER: Shaheen A
PROVIDER: S-EPMC11459071 | biostudies-literature | 2025 Jan-Feb
REPOSITORIES: biostudies-literature