Leveraging Large Language Models in Extracting Drug Safety Information from Prescription Drug Labels.

ABSTRACT:

Introduction

Adverse drug reactions (ADRs), including those resulting from drug interactions, remain a leading cause of morbidity and mortality. Structured product labels (SPLs) serve as a primary source for drug safety information. Having machine-readable product labels, including adverse reactions (ARs) and drug interactions, readily available would allow researchers to streamline medication safety studies. However, extracting this information is complex and requires the use of natural language processing (NLP) methods.

Objective

In this study, we explored the application of generative language models in the extraction of drug safety information from SPLs.

Methods

We compared multiple generative LLMs (GPT, Llama, and Mixtral) to two baseline methods in the task of e

SUBMITTER: Gisladottir U 

PROVIDER: S-EPMC12860809 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature