Synthetic or Authentic? Building Mental Patient Simulators from Longitudinal Evidence

2026-03-23

AI Summary

Current mental health conversational systems often rely on static prompts for patient simulators, resulting in homogeneous behaviors and incoherent symptom progression across multi-turn interactions. To address this limitation, this work proposes the DEPROFILE framework, which uniquely integrates real-world longitudinal clinical and life-event data into patient simulation. By fusing multi-source information into a unified patient profile and introducing a Chain-of-Change agent that transforms noisy event logs into structured temporal memories, DEPROFILE significantly enhances the realism, behavioral diversity, and temporal consistency of simulated patients. Extensive evaluations demonstrate that the proposed approach consistently outperforms state-of-the-art baselines across multiple large language model backbones.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Patient simulation is essential for developing and evaluating mental health dialogue systems. As most existing approaches rely on snapshot-style prompts with limited profile information, homogeneous behaviors and incoherent disease progression in multi-turn interactions have become key chellenges. In this work, we propose DEPROFILE, a data-grounded patient simulation framework that constructs unified, multi-source patient profiles by integrating demographic attributes, standardized clinical symptoms, counseling dialogues, and longitudinal life-event histories from real-world data. We further introduce a Chain-of-Change agent to transform noisy longitudinal records into structured, temporally grounded memory representations for simulation. Experiments across multiple large language model (LLM) backbones show that with more comprehensive profile constructed by DEPROFILE, the dialogue realism, behavioral diversity, and event richness have consistently improved and exceed state-of-the-art baselines, highlighting the importance of grounding patient simulation in verifiable longitudinal evidence.

Problem

• patient simulation
• mental health dialogue systems
• longitudinal evidence
• behavioral coherence
• disease progression

Innovation

• patient simulation
• longitudinal data
• mental health dialogue systems
• Chain-of-Change
• data-grounded profiling