用多智能体模拟肥胖伴心理疾病患者，研究行为与治疗反应。
SynthAgent: A Multi-Agent LLM Framework for Realistic Patient Simulation -- A Case Study in Obesity with Mental Health Comorbidities
- 构建多智能体系统，融合临床数据与人格特质生成虚拟病人。
- 100+虚拟患者验证中，GPT-5与Claude 4.5 Sonnet表现最优。
- 适合医学模拟、心理健康研究及隐私敏感场景使用。
高保真患者模拟为研究复杂疾病提供了强大途径，可缓解真实数据碎片化、偏倚和隐私限制问题。本研究提出SynthAgent，一种多智能体系统（MAS）框架，用于建模伴有抑郁、焦虑、社交恐惧及暴食症等共病的心理健康障碍肥胖患者。该框架整合医保数据、人群调查与以患者为中心的文献中的临床证据，构建具有影响依从性、情绪调节与生活方式行为的人格特质的个性化虚拟患者。通过智能体自主交互，系统模拟疾病进展、治疗反应与多元社会心理背景下的生活管理。对超过100名生成患者的评估表明，以GPT-5和Claude 4.5 Sonnet为核心引擎的MAS在仿真保真度上优于Gemini 2.5 Pro与DeepSeek-R1。SynthAgent因此提供了一种可扩展且保护隐私的框架，用于探索医疗与心理领域的患者旅程、行为动态与决策过程。
原文摘要 · Abstract (English)
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.
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