Personalized nutrition intervention for patients with multimorbidity is critical for improving health outcomes, yet remains challenging because it requires the simultaneous integration of heterogeneous clinical conditions, medications, and dietary guidelines. Single-agent large language models (LLMs) often suffer from context overload and attention dilution when processing such high-dimensional patient profiles. We introduce NutriOrion, a hierarchical multi-agent framework with a parallel-then-sequential reasoning topology. NutriOrion decomposes nutrition planning into specialized domain agents with isolated contexts to mitigate anchoring bias, followed by a conditional refinement stage. The framework includes a multi-objective prioritization algorithm to resolve conflicting dietary requirements and a safety constraint mechanism that injects pharmacological contraindications as hard negative constraints during synthesis, ensuring clinical validity by construction rather than post-hoc filtering. For clinical interoperability, NutriOrion maps synthesized insights into the ADIME standard and FHIR R4 resources. Evaluated on 330 stroke patients with multimorbidity, NutriOrion outperforms multiple baselines, including GPT-4.1 and alternative multi-agent architectures. It achieves a 12.1 percent drug-food interaction violation rate, demonstrates strong personalization with negative correlations (-0.26 to -0.35) between patient biomarkers and recommended risk nutrients, and yields clinically meaningful dietary improvements, including a 167 percent increase in fiber and a 27 percent increase in potassium, alongside reductions in sodium (9 percent) and sugars (12 percent).

翻译：针对共病患者进行个性化营养干预对于改善健康结局至关重要，但由于需要同时整合异质性临床状况、药物和饮食指南，这仍然具有挑战性。单智能体大语言模型在处理此类高维患者档案时，常常面临上下文过载和注意力稀释的问题。我们提出了NutriOrion，一个具有并行-顺序推理拓扑结构的分层多智能体框架。NutriOrion将营养规划分解为具有隔离上下文的专业领域智能体，以减轻锚定偏差，随后进行条件细化阶段。该框架包含一个多目标优先级排序算法，用于解决冲突的饮食要求，以及一个安全约束机制，该机制在合成过程中将药理学禁忌症作为硬性负约束注入，通过构建而非事后过滤来确保临床有效性。为实现临床互操作性，NutriOrion将合成的见解映射到ADIME标准和FHIR R4资源。在330名患有共病的卒中患者上进行评估，NutriOrion的表现优于包括GPT-4.1和替代多智能体架构在内的多个基线。其药物-食物相互作用违规率为12.1%，表现出强大的个性化能力，患者生物标志物与推荐的风险营养素之间呈负相关（-0.26至-0.35），并产生了具有临床意义的饮食改善，包括纤维增加167%、钾增加27%，同时钠减少9%、糖减少12%。