Applications of Federated Large Language Model for Adverse Drug Reactions Prediction: Scoping Review.

ABSTRACT:

Background

Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information.

Objective

This review aims to explore the potential of federated learning (FL) combined with natural language processing and large language models (LLMs) to enhance ADR prediction. FL enables decentralized training across client clusters with limited resources, while LLMs effectively process unstructured health care data. By aggregating client-trained models into a global model, FL ensures broader da

SUBMITTER: Guo D 

PROVIDER: S-EPMC12516295 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature