Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Nov 1, 2024
Date Accepted: Apr 12, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Applications of Federated Large Language Model for Adverse Drug Reactions Prediction: A Scoping Review
ABSTRACT
Background:
Adverse drug reactions (ADRs) pose significant challenges in healthcare, where early prevention is vital for effective treatment and patient safety.
Objective:
Traditional supervised learning methods are limited in addressing healthcare data, which is often unstructured, heavily regulated, and involves restricted access to sensitive personal information.
Methods:
The integration of Federated Learning (FL) and Large Language Model (LLM) offers a promising solution to these challenges since FL supports the distributed training on edge device with limited resources and the capability of LLM to deal with unstructured healthcare data. Additionally, client models trained on the edge device can be merged into a global model on the server, preserving data privacy.
Results:
Natural Language Processing (NLP) technologies underpinning LLM provide a full set of tools that can readily be used to process unstructured ADR as input, enabling LLM to predict ADR outcome effectively. The ADR output space can be discrete labels, unstructured texts, or both.
Conclusions:
This review presents a scoping review following the PRISMA protocol on the applications of Federated Large Language Model (FedLLM) in ADR prediction, aiming to explore future research venue on ADR applications
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