Abstract
Cardiovascular disease (CVD) risk stratification remains a major challenge due to its multifactorial nature and limited availability of high-quality labeled datasets. While genomic and electrophysiological data such as SNP variants and ECG phenotypes are increasingly accessible, effectively integrating these modalities in low-label settings is non-trivial. This challenge arises from the scarcity of well-annotated multimodal datasets and the high dimensionality of biological signals, which limit the effectiveness of conventional supervised models. To address this, we present a few-label multimodal framework that leverages large language models (LLMs) to combine genetic and electrophysiological information for cardiovascular risk stratification. Our approach incorporates a pseudo-label refinement strategy to adaptively distill high-confidence labels from weakly supervised predictions, enabling robust model fine-tuning with only a small set of ground-truth annotations. To enhance the interpretability, we frame the task as a Chain of Thought (CoT) reasoning problem, prompting the model to produce clinically relevant rationales alongside predictions. Experimental results demonstrate that the integration of multimodal inputs, few-label supervision, and CoT reasoning improves robustness and generalizability across diverse patient profiles. Experimental results using multimodal SNP variants and ECG-derived features demonstrated comparable performance to models trained on the full dataset, underscoring the promise of LLM-based few-label multimodal modeling for advancing personalized cardiovascular care.
Abstract (translated)
心血管疾病（CVD）的风险分层仍然是一个重大挑战，因为其多因素性质以及高质量标注数据的缺乏。尽管基因组和电生理学数据如单核苷酸多态性（SNP）变异和心电图表型越来越容易获得，但在标签较少的情况下有效地整合这些模态却并不简单。这一问题源于多模式注释数据稀缺及生物信号高维度的特点，这限制了传统监督模型的有效性。 为了解决这个问题，我们提出了一种少标签的多模态框架，该框架利用大型语言模型（LLM）结合基因和电生理信息进行心血管风险分层。我们的方法包括伪标签精炼策略，可以从弱监督预测中自适应地提炼出高置信度标签，从而使仅用少量真实标注的情况下也能实现稳健的模型微调。 为了提高可解释性，我们将任务构架为链式思维（CoT）推理问题，促使模型在给出预测的同时提供临床相关理由。实验结果表明，多模态输入、少标签监督和CoT推理可以增强模型在不同患者群体中的鲁棒性和泛化能力。使用多模态SNP变异和心电图派生特征进行的实验结果显示，该方法的表现与基于完整数据集训练的模型相当，这彰显了LLM为基础的少标签多模式建模技术在推进个性化心血管护理方面所展现的巨大潜力。
URL
https://arxiv.org/abs/2510.16536
