用智能体自动发现适合每位患者的心脏电生理模型结构。
Learning Cardiac Electrophysiology Digital Twins Through Agentic Discovery of Hybrid Structure
- 让大模型智能体在结构空间中迭代试错，自动组合物理与神经网络模型。
- 在真实和合成数据上均超越人工设计与现有大模型方法，预测误差更低。
- 适合需要个性化建模的临床研究者和计算心脏病学团队。
构建个性化的心脏电生理（EP）数字孪生，关键在于为每位患者识别合适的模型结构，而不仅是参数拟合。传统方法依赖专家手动设计混合物理-神经架构，需深厚领域知识且难以跨患者迁移。近期工作尝试用大语言模型（LLMs）生成或充当混合模型，但其缺乏稳定模拟所需的结构先验。为此，我们提出LEADS框架，将心脏电生理领域知识转化为结构化动作空间，并利用LLM智能体发现混合模型。该智能体通过迭代推理-行动循环，选择、组合并优化候选模型，同时由梯度下降完成参数拟合。所提LEADS确保每个候选模型具备物理合理性、可解释性与数值稳定性，同时支持开放式的架构探索。我们在三种真实反应模型的合成数据及真实心脏电生理数据上验证了LEADS，结果表明其性能优于人工设计的混合模型和其他基于LLM的方法。
原文摘要 · Abstract (English)
Building personalized cardiac electrophysiology (EP) digital twins requires identifying the appropriate model structure for each patient, not merely fitting parameters. Traditional methods rely on experts to manually prescribe hybrid physics-neural architectures, which requires deep domain expertise and does not transfer across patients. Recent works have applied large language models (LLMs) to generate or act as hybrid models. However, despite their promising generalization capacity, these LLM-based methods lack the structural priors needed for stable cardiac simulations. Hence, we propose LEADS, a framework that formulates cardiac EP domain knowledge as a structured action space and utilizes an LLM agent to discover hybrid models. The agent follows an iterative reasoning-and-action loop to select, combine, and refine hybrid models, whilst gradient descent handles parameter fitting. The proposed LEADS designs every candidate model towards physically grounded, interpretable, and numerically stable, while allowing open-ended architectural discovery. We validate LEADS on synthetic data with three ground-truth reaction models and on real cardiac EP data, demonstrating that it outperforms both human-designed hybrid models and other LLM-based hybrid modeling.
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