ECG-WM: A Physiology-Informed ECG World Model for Clinical Intervention Simulation

2026-05-17

AI Summary

This work addresses the challenge that existing electrocardiogram (ECG) models struggle to capture the evolving electrophysiological dynamics of the heart under external interventions such as pharmacological treatments. To overcome this limitation, the authors propose the first physiology-informed, action-conditioned ECG world model, which structurally embeds ordinary differential equation (ODE)-based physiological mechanisms into latent-space diffusion dynamics through energy-based regularization, enabling credible prediction of post-intervention ECG trajectories. The study further introduces a novel uncertainty-aware evaluation strategy grounded in sampling stochasticity to quantify clinical risk and its variability. Experiments on real-world clinical and drug-response datasets demonstrate that the proposed method significantly improves waveform fidelity and risk calibration, with predictions closely aligned with expert therapeutic preferences.

Abstract

Electrocardiogram (ECG)-based models have achieved strong performance in diagnostic tasks, yet they remain limited in modeling how cardiac dynamics evolve under external interventions. In particular, existing approaches focus primarily on static prediction and lack mechanisms to capture ECG variations under different pharmacological conditions. In this work, we propose an ECG World Model for action-conditioned predictive simulation of cardiac electrophysiology. Moving beyond disjoint pipelines, our framework features a principled integration of physiological ordinary differential equation (ODE) priors into latent diffusion dynamics via energy regularization. This structural constraint enables the synthesis of physiologically plausible post-intervention ECG trajectories while effectively mitigating generative hallucinations. Building on this simulation process, we introduce an uncertainty-aware evaluation strategy that leverages the stochasticity of diffusion sampling to characterize both the expected clinical risk and its variability, allowing a more reliable comparative assessment of candidate interventions. We evaluate our method across diverse settings, including controlled drug-response scenarios and real-world clinical records. Beyond standard waveform metrics, experimental results demonstrate improved risk calibration and strong alignment with expert-informed treatment preferences. These results establish our approach as a robust foundation for safe and intervention-aware clinical decision support.

Problem

• ECG
• clinical intervention
• cardiac dynamics
• pharmacological conditions
• predictive simulation

Innovation

• ECG World Model
• physiology-informed modeling
• action-conditioned simulation
• latent diffusion dynamics
• uncertainty-aware evaluation