用大语言模型引导基于IoT的医疗保健警报系统
Guiding IoT-Based Healthcare Alert Systems with Large Language Models
AI总结：
提出LLM-HAS框架，将LLM融入资源受限的IoT医疗警报系统，利用MoE分析用户偏好与风险并指导DDPG专家做出精确警报，同时处理对话反馈以微调模型，在保护隐私下提升警报准确性与个性化。
摘要：
医疗保健警报系统（HAS）正经历快速演进，其驱动力来自人工智能（AI）、物联网（IoT）技术的进步以及日益增强的健康意识。尽管取得了重大进展，但一个根本性挑战依然存在：在资源受限的HAS环境中，平衡个性化健康警报的准确性与严格的隐私保护。为解决这一问题，我们引入了一个统一框架LLM-HAS，该框架将大语言模型（LLM）融入HAS，以显著提升准确性、确保用户隐私、增强个性化健康服务，同时改善用户的主观体验质量（QoE）。我们的创新框架采用混合专家（MoE）方法，并由LLM增强，用于从额外的文本工作描述中分析用户的个性化偏好和潜在健康风险。该分析指导选择专门的深度强化学习（DDPG）专家，负责做出精确的健康警报。此外，LLM-HAS能够处理对话式用户反馈，这不仅允许对DDPG进行微调，还加深了用户参与度，从而增强健康管理策略的准确性和个性化。仿真结果验证了LLM-HAS框架的有效性，凸显了其作为采用生成式AI（GAI）提供高度准确且可靠警报的开创性方法的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
