Guiding IoT-Based Healthcare Alert Systems with Large Language Models

TL;DR

This paper presents LLM-HAS, a novel framework integrating Large Language Models with IoT healthcare alert systems to improve accuracy, privacy, and personalization through a mixture of experts and reinforcement learning, validated by simulations.

Contribution

The paper introduces a unified LLM-based framework for IoT healthcare alerts that enhances accuracy, privacy, and personalization using a mixture of experts and conversational feedback.

Findings

Significant improvement in alert accuracy and personalization.

Effective privacy protection in resource-constrained environments.

Enhanced user engagement through conversational feedback.

Abstract

Healthcare alert systems (HAS) are undergoing rapid evolution, propelled by advancements in artificial intelligence (AI), Internet of Things (IoT) technologies, and increasing health consciousness. Despite significant progress, a fundamental challenge remains: balancing the accuracy of personalized health alerts with stringent privacy protection in HAS environments constrained by resources. To address this issue, we introduce a uniform framework, LLM-HAS, which incorporates Large Language Models (LLM) into HAS to significantly boost the accuracy, ensure user privacy, and enhance personalized health service, while also improving the subjective quality of experience (QoE) for users. Our innovative framework leverages a Mixture of Experts (MoE) approach, augmented with LLM, to analyze users' personalized preferences and potential health risks from additional textual job descriptions. This analysis guides the selection of specialized Deep Reinforcement Learning (DDPG) experts, tasked with making precise health alerts. Moreover, LLM-HAS can process Conversational User Feedback, which not only allows fine-tuning of DDPG but also deepen user engagement, thereby enhancing both the accuracy and personalization of health management strategies. Simulation results validate the effectiveness of the LLM-HAS framework, highlighting its potential as a groundbreaking approach for employing generative AI (GAI) to provide highly accurate and reliable alerts.