MetaPlate: 反事实引导的RAG-LLM工具用于个性化食物推荐和高血糖预防
MetaPlate: Counterfactual-Guided RAG-LLM Tool for Personalized Food Recommendation and Hyperglycemia Prevention
AI总结：
提出MetaPlate框架，结合反事实解释、机器学习预测和RAG-LLM，生成个性化膳食建议以预防餐后高血糖，经注册营养师评估证明其可行性和有效性。
摘要：
餐后高血糖是代谢紊乱的关键风险因素；然而，现有的饮食指导通常是静态的、不切实际的且个性化不足，提供的建议难以遵循或效果不佳。尽管最近的进展利用连续血糖监测（CGM）和机器学习来预测血糖反应，但这些方法主要是预测性的，缺乏可操作的指导。此外，推荐系统常常与用户目标不一致，且需要大量输入。我们提出了MetaPlate，一个反事实解释（CF）引导的、上下文感知的决策支持框架，用于生成个性化膳食建议，以减轻健康成年人的餐后血糖波动。MetaPlate整合了多模态数据，包括来自25名个体的CGM读数、可穿戴设备衍生的生理信号以及用户提供的膳食输入，以建模餐前上下文。一个机器学习模型预测血糖反应，而CF优化模块通过调整膳食组成（修改宏量营养素数量）来维持血糖水平在目标范围内（≤140 mg/dL）。基于LLM的检索增强生成（RAG）层通过使用USDA食品数据库的约束搜索生成人类可读的建议，增强了可解释性。我们通过结构化的专家在环评估，与注册营养师（RDs）一起评估MetaPlate，比较提示优化前后的性能。结果显示，在膳食真实性、份量适宜性和推荐可能性方面有所改进，专家反馈表明从临床不可行的输出转向了可操作、上下文适宜的建议。我们的发现强调了领域知识和结构化约束在LLM驱动系统中的重要性，并突出了MetaPlate作为实时个性化膳食决策支持工具的潜力。
英文摘要：
Postprandial hyperglycemia is a key risk factor for metabolic disorders; however, existing dietary guidance is often static, impractical, and insufficiently personalized, providing recommendations that are difficult to follow or not impactful. While recent advances leverage continuous glucose monitoring (CGM) and machine learning to predict glycemic responses, these approaches are largely predictive and lack actionable guidance. Moreover, recommendation systems are often misaligned with user goals and require extensive input. We present MetaPlate, a counterfactual explanation (CF) guided, context-aware decision-support framework that generates personalized meal recommendations to mitigate postprandial glucose excursions in healthy adults. MetaPlate integrates multimodal data, including CGM readings, wearable-derived physiological signals, and user-provided meal inputs from $25$ individuals to model pre-meal context. A machine learning model predicts glucose response, while a CF optimization module adjusts meal composition modifying macronutrient amounts to maintain glucose levels within a target range ($\leq 140$ mg/dL). An LLM-based retrieval-augmented generation (RAG) layer enhances interpretability by producing human-readable recommendations using constrained search of the USDA food database. We evaluate MetaPlate via a structured expert-in-the-loop assessment with registered dietitians (RDs), comparing performance before and after prompt refinement. Results show improvements in meal realism, portion suitability, and recommendation likelihood, with expert feedback indicating a shift from clinically implausible outputs to actionable, contextually appropriate recommendations. Our findings emphasize the importance of domain knowledge and structured constraints in LLM-driven systems and highlight the potential of MetaPlate as a real-time personalized dietary decision-support tool.
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