Large Language Models for Opioid-Induced Respiratory Depression Prediction in Hospitalized Patients: A Retrospective Study
Year: 2025
Type: article
Abstract: Opioid-induced adverse events pose significant risks to hospitalized patients. However, there is a limited understanding of which patients on general care floors are at risk for Opioid-Induced Respiratory Depression (OIRD). This study aims to bridge that knowledge gap by utilizing the advancements of AI to interpret Electronic Medical Records (EMRs). Recently, Large Language Models (LLMs) have gained attention for their exceptional capabilities in understanding human language, which makes them crucial for AI systems in healthcare that focus on clinical narratives. In this study, we extracted 2,663 hospitalized adult patient records from UC Davis Medical Center archives between January 2010 and April 2020 to identify patients at high risk of OIRD. For this purpose, we employed clinical language models (BioBERT, ClinicalBERT, and GatorTron) and fine-tuned them on the OIRD dataset. Additionally, we leveraged the capabilities of GPT-4, a state-of-the-art LLM, to select the most informative risk factors and enhance the accuracy of the predictive models.
DOI: 10.1145/3712302
Authors: Elahe Hosseini, Abhinav Srinivas, Najmeh Nazari, Charity Hale, Setareh Rafatirad, Houman Homayoun
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408684135