Simulating high-fidelity patients offers a powerful avenue for studying complex diseases while addressing the challenges of fragmented, biased, and privacy-restricted real-world data. In this study, we introduce SynthAgent, a novel Multi-Agent System (MAS) framework designed to model obesity patients with comorbid mental disorders, including depression, anxiety, social phobia, and binge eating disorder. SynthAgent integrates clinical and medical evidence from claims data, population surveys, and patient-centered literature to construct personalized virtual patients enriched with personality traits that influence adherence, emotion regulation, and lifestyle behaviors. Through autonomous agent interactions, the system simulates disease progression, treatment response, and life management across diverse psychosocial contexts. Evaluation of more than 100 generated patients demonstrated that GPT-5 and Claude 4.5 Sonnet achieved the highest fidelity as the core engine in the proposed MAS framework, outperforming Gemini 2.5 Pro and DeepSeek-R1. SynthAgent thus provides a scalable and privacy-preserving framework for exploring patient journeys, behavioral dynamics, and decision-making processes in both medical and psychological domains.

翻译：模拟高保真患者为研究复杂疾病提供了有力途径，同时解决了真实世界数据碎片化、存在偏见且受隐私限制的挑战。本研究提出了SynthAgent，一种新颖的多智能体系统框架，旨在模拟患有抑郁症、焦虑症、社交恐惧症和暴食障碍等共病精神障碍的肥胖症患者。SynthAgent整合了来自理赔数据、人口调查和以患者为中心的文献的临床与医学证据，构建出个性化的虚拟患者，这些患者被赋予了影响治疗依从性、情绪调节和生活方式行为的个性化特征。通过自主智能体交互，该系统模拟了不同社会心理背景下的疾病进展、治疗反应和生活管理。对超过100名生成患者的评估表明，GPT-5和Claude 4.5 Sonnet作为所提出MAS框架的核心引擎实现了最高的保真度，其表现优于Gemini 2.5 Pro和DeepSeek-R1。因此，SynthAgent为探索医学和心理领域的患者历程、行为动态和决策过程，提供了一个可扩展且保护隐私的框架。