SynthAgent: A Multi-Agent LLM Framework for Realistic Patient Simulation -- A Case Study in Obesity with Mental Health Comorbidities

• URL: http://arxiv.org/abs/2602.08254v1
• Date: Mon, 09 Feb 2026 04:14:19 GMT
• Title: SynthAgent: A Multi-Agent LLM Framework for Realistic Patient Simulation -- A Case Study in Obesity with Mental Health Comorbidities
• Authors: Arman Aghaee, Sepehr Asgarian, Jouhyun Jeon,
• Abstract summary: We introduce SynthAgent, a novel Multi-Agent System (MAS) framework to model obesity patients with comorbid mental disorders.<n> SynthAgent integrates clinical and medical evidence from claims data, population surveys, and patient-centered literature.<n>The system simulates disease progression, treatment response, and life management across diverse psychosocial contexts.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Patient Digital Twins for Chronic Care: Technical Hurdles, Lessons Learned, and the Road Ahead [0.0]
  Chronic diseases constitute the principal burden of morbidity, mortality and healthcare costs worldwide.<n>Patient Medical Digital Twins (PMDTs) offer a paradigm shift: holistic, continuously updated digital counterparts of patients that integrate clinical, genomic, lifestyle, and quality-of-life data.
  arXiv  Detail & Related papers  (2026-02-11T13:07:00Z)
• A Semantic Framework for Patient Digital Twins in Chronic Care [0.0]
  The Patient Medical Digital Twin (PMDT) integrates physiological, psychosocial, behavioral, and genomic information into a coherent model.<n>The PMDT ensures semantic interoperability, supports automated reasoning, and enables reuse across diverse clinical contexts.<n>By bridging gaps in data fragmentation and semantic standardization, the PMDT provides a validated foundation for next-generation digital health ecosystems.
  arXiv  Detail & Related papers  (2025-10-10T08:34:55Z)
• Patient-Zero: A Unified Framework for Real-Record-Free Patient Agent Generation [11.75912414451272]
  We propose a realistic patient generation framework, Patient-Zero, which requires no real medical records.<n>Patient-Zero first introduces a medically-aligned multi-step generation architecture, which builds comprehensive patient records through hierarchical medical knowledge injection without real medical records.<n>Our framework enables the generation of contextually diverse patient records while maintaining strict medical coherence, supported by adaptive dialogue strategies and real-time clinical plausibility verification.
  arXiv  Detail & Related papers  (2025-09-14T03:56:00Z)
• Organ-Agents: Virtual Human Physiology Simulator via LLMs [66.40796430669158]
  Organ-Agents is a multi-agent framework that simulates human physiology via LLM-driven agents.<n>We curated data from 7,134 sepsis patients and 7,895 controls, generating high-resolution trajectories across 9 systems and 125 variables.<n>Organ-Agents achieved high simulation accuracy on 4,509 held-out patients, with per-system MSEs 0.16 and robustness across SOFA-based severity strata.
  arXiv  Detail & Related papers  (2025-08-20T01:58:45Z)
• A Comprehensive Review of Datasets for Clinical Mental Health AI Systems [55.67299586253951]
  We present the first comprehensive survey of clinical mental health datasets relevant to the training and development of AI-powered clinical assistants.<n>Our survey identifies critical gaps such as a lack of longitudinal data, limited cultural and linguistic representation, inconsistent collection and annotation standards, and a lack of modalities in synthetic data.
  arXiv  Detail & Related papers  (2025-08-13T13:42:35Z)
• AI Agents for Conversational Patient Triage: Preliminary Simulation-Based Evaluation with Real-World EHR Data [3.4206930658402115]
  We present a Patient Simulator that leverages real world patient encounters.<n>The simulator provides a realistic approach to patient presentation and multi-turn conversation with a symptom-checking agent.
  arXiv  Detail & Related papers  (2025-06-04T14:56:08Z)
• MoodAngels: A Retrieval-augmented Multi-agent Framework for Psychiatry Diagnosis [58.67342568632529]
  MoodAngels is the first specialized multi-agent framework for mood disorder diagnosis.<n>MoodSyn is an open-source dataset of 1,173 synthetic psychiatric cases.
  arXiv  Detail & Related papers  (2025-06-04T09:18:25Z)
• Beyond Empathy: Integrating Diagnostic and Therapeutic Reasoning with Large Language Models for Mental Health Counseling [50.83055329849865]
  PsyLLM is a large language model designed to integrate diagnostic and therapeutic reasoning for mental health counseling.<n>It processes real-world mental health posts from Reddit and generates multi-turn dialogue structures.<n>Our experiments demonstrate that PsyLLM significantly outperforms state-of-the-art baseline models.
  arXiv  Detail & Related papers  (2025-05-21T16:24:49Z)
• MAGI: Multi-Agent Guided Interview for Psychiatric Assessment [50.6150986786028]
  We present MAGI, the first framework that transforms the gold-standard Mini International Neuropsychiatric Interview (MINI) into automatic computational navigation.<n>We show that MAGI advances LLM- assisted mental health assessment by combining clinical rigor, conversational adaptability, and explainable reasoning.
  arXiv  Detail & Related papers  (2025-04-25T11:08:27Z)
• Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [58.61680631581921]
  Mental health disorders create profound personal and societal burdens, yet conventional diagnostics are resource-intensive and limit accessibility.<n>This paper examines these challenges and proposes solutions, including anonymization, synthetic data, and privacy-preserving training.<n>It aims to advance reliable, privacy-aware AI tools that support clinical decision-making and improve mental health outcomes.
  arXiv  Detail & Related papers  (2025-02-01T15:10:02Z)
• Safe and Interpretable Estimation of Optimal Treatment Regimes [54.257304443780434]
  We operationalize a safe and interpretable framework to identify optimal treatment regimes. Our findings support personalized treatment strategies based on a patient's medical history and pharmacological features.
  arXiv  Detail & Related papers  (2023-10-23T19:59:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.