Transformer-based models for ADR detection: cross-drug validation and benchmarking against large language models.

ABSTRACT:

Background

Adverse drug reactions (ADRs) are harmful side effects of medications. Social media provides real-time, patient-generated data, though its unstructured format presents challenges. Natural language processing and transfer learning offer promising solutions.

Objective

This study aimed to evaluate whether transformer-based models fine-tuned on a general ADR dataset can effectively classify ADRs from tweets related to glucagon-like peptide-1 (GLP-1) receptor agonists and to benchmark their performance against state-of-the-art large language models (LLMs).

Design

This study employed a machine learning approach using transformer-based language models to classify ADRs in social media.

Methods

BERT (bidirectional encoder representations from transformers)-ba

SUBMITTER: Kim M 

PROVIDER: S-EPMC12715130 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature