Using Large Language Models to Detect and Understand Drug Discontinuation Events in Web-Based Forums: Development and Validation Study.

ABSTRACT:

Background

The implementation of large language models (LLMs), such as BART (Bidirectional and Auto-Regressive Transformers) and GPT-4, has revolutionized the extraction of insights from unstructured text. These advancements have expanded into health care, allowing analysis of social media for public health insights. However, the detection of drug discontinuation events (DDEs) remains underexplored. Identifying DDEs is crucial for understanding medication adherence and patient outcomes.

Objective

The aim of this study is to provide a flexible framework for investigating various clinical research questions in data-sparse environments. We provide an example of the utility of this framework by identifying DDEs and their root causes in an open-source web-based forum, MedHelp, an

SUBMITTER: Trevena W 

PROVIDER: S-EPMC11826943 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature