量化隐藏盐分以实现精准医疗：基于联合因子检索与思维链推理的钠摄入评估
Quantifying Hidden Salt for Precision Healthcare: Sodium Assessment via Joint-Factor Retrieval and Chain-of-Thought Inference
- University of Chinese Academy of Sciences（中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对食谱中隐藏钠难以监测的问题，提出SALT框架，结合联合因子检索与4跳思维链推理，在SALT54k数据集上实现最先进的钠含量估计，助力精准医疗。
AI中文摘要：
精准医疗，特别是针对高血压和心血管疾病等病症，需要对饮食中的钠摄入量进行监测。然而，由于烹饪中隐藏盐分的普遍存在，例如酱油和番茄酱中的钠，使得这一监测工作受到阻碍。尽管食谱为饮食分析提供了宝贵的数据来源，但富含钠的调味料在制作说明中经常被省略或描述得含糊不清。为解决这一问题，我们提出了SALT，一个钠评估与水平跟踪框架，采用RAG框架来评估食谱中的钠含量。我们的框架首先引入了一个联合因子嵌入检索模块，以定位具有特定钠含量的相似食谱，从而解决缺乏上下文参考的问题。这些检索到的样本为后续推理提供了上下文。随后，我们设计了一个结构化的4跳思维链推理模块，通过多步骤的钠估计来细化语言模型产生的模糊估计。为促进我们的研究，我们进一步构建了一个食谱数据集SALT54k，其中包含54,151条条目，标注了11种常见调味料的钠含量。在SALT54k上的结果表明，我们的方法在钠估计方面达到了最先进的性能。额外的现实世界验证证实了我们方法的有效性，展示了其作为AI辅助精准医疗实际解决方案的潜力。
英文摘要：
Precision healthcare, particularly for conditions like hypertension and cardiovascular disease, necessitates monitoring of dietary sodium intake. However, tracking this is hindered by the prevalence of hidden salt in cooking, such as sodium in soy sauce and ketchup. While recipes offer a valuable data source for dietary analysis, sodium-rich seasonings are frequently omitted or described ambiguously in instructions. To solve this issue, we propose SALT, a Sodium Assessing & Level Tracking framework adopting an RAG framework to assess sodium content in recipes. Our framework first introduces a Joint-Factor Embedding Retrieval module to locate similar recipes with specified sodium content for addressing the lack of contextual references. These retrieved samples provide contexts for subsequent inference. Then we design a structured 4-hop Chain-of-Thought inference module to refine the vague estimation from language models through a multi-step sodium estimation. To facilitate our study, we further construct a recipe dataset SALT54k with $54,151$ entries labeled with sodium quantities across $11$ common seasonings. Results on SALT54k demonstrate that our method achieves state-of-the-art performance in sodium estimation. Additional real-world validations confirm the effectiveness of our method, demonstrating its potential as a practical solution for AI-assisted precision healthcare.
