MM-DADM: Multimodal Drug-Aware Diffusion Model for Virtual Clinical Trials

TL;DR

This paper introduces MM-DADM, a novel generative model for creating individualized drug-induced ECGs, addressing key challenges in realism, pathology, and data scarcity in virtual clinical trials.

Contribution

The paper presents the first multimodal, drug-aware diffusion framework with causal disentanglement and counterfactual augmentation for ECG generation.

Findings

Outperforms 10 state-of-the-art ECG generation models.

Improves simulation accuracy by at least 6.13%.

Enhances downstream classification performance.

Abstract

High failure rates in cardiac drug development necessitate virtual clinical trials via electrocardiogram (ECG) generation to reduce risks and costs. However, existing ECG generation models struggle to balance morphological realism with pathological flexibility, fail to disentangle demographics from genuine drug effects, and are severely bottlenecked by early-phase data scarcity. To overcome these hurdles, we propose the Multimodal Drug-Aware Diffusion Model (MM-DADM), the first generative framework for generating individualized drug-induced ECGs. Specifically, our proposed MM-DADM integrates a Dynamic Cross-Attention (DCA) module that adaptively fuses External Physical Knowledge (EPK) to preserve morphological realism while avoiding the suppression of complex pathological nuances. To resolve feature entanglement, a Causal Feature Encoder (CFE) actively filters out demographic noise to extract pure pharmacological representations. These representations subsequently guide a Causal-Disentangled ControlNet (CDC-Net), which leverages counterfactual data augmentation to explicitly learn intrinsic pharmacological mechanisms despite limited clinical data. Extensive experiments on $9,443$ ECGs across $8$ drug regimens demonstrate that MM-DADM outperforms $10$ state-of-the-art ECG generation models, improving simulation accuracy by at least $6.13\%$ and recall by $5.89\%$, while providing highly effective data augmentation for downstream classification tasks.