Clinical Sentiment Analysis by Large Language Models Enhances Prediction of Hepatorenal Syndrome.

ABSTRACT:

Background and aims

Hepatorenal syndrome-acute kidney injury (HRS-AKI) is a severe complication of decompensated cirrhosis that is challenging to predict. Large language models are robust for natural language processing (NLP). We aimed to determine if clinical sentiment in unstructured text can be derived by large language models and used to improve prediction of HRS-AKI over traditional predictors alone.

Methods

Cohort study consisting of adult patients admitted to a single academic medical center with decompensated cirrhosis and AKI. We used Microsoft Azure OpenAI generative pretrained transformer (GPT)-4o to derive a sentiment score ranging from 0 to 1 for HRS-AKI and conduct NLP extraction of clinical terms associated with HRS-AKI in admission notes. The area under the r

SUBMITTER: Lai M 

PROVIDER: S-EPMC12547914 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature