Heart failure (HF) poses a significant public health challenge, with a rising global mortality rate. Early detection and prevention of HF could significantly reduce its impact. We introduce a novel methodology for predicting HF risk using 12-lead electrocardiograms (ECGs). We present a novel, lightweight dual-attention ECG network designed to capture complex ECG features essential for early HF risk prediction, despite the notable imbalance between low and high-risk groups. This network incorporates a cross-lead attention module and twelve lead-specific temporal attention modules, focusing on cross-lead interactions and each lead's local dynamics. To further alleviate model overfitting, we leverage a large language model (LLM) with a public ECG-Report dataset for pretraining on an ECG-report alignment task. The network is then fine-tuned for HF risk prediction using two specific cohorts from the UK Biobank study, focusing on patients with hypertension (UKB-HYP) and those who have had a myocardial infarction (UKB-MI).The results reveal that LLM-informed pre-training substantially enhances HF risk prediction in these cohorts. The dual-attention design not only improves interpretability but also predictive accuracy, outperforming existing competitive methods with C-index scores of 0.6349 for UKB-HYP and 0.5805 for UKB-MI. This demonstrates our method's potential in advancing HF risk assessment with clinical complex ECG data.

翻译：心力衰竭（HF）是一项重大的公共卫生挑战，其全球死亡率持续上升。早期检测和预防心力衰竭可显著减轻其影响。我们提出了一种利用十二导联心电图（ECG）预测心力衰竭风险的新方法。我们设计了一种新型轻量级双注意力心电图网络，能够捕捉早期心力衰竭风险预测所需的关键复杂心电图特征，尽管低风险组与高风险组之间存在显著的不平衡。该网络包含一个跨导联注意力模块和十二个导联特异性时间注意力模块，分别关注跨导联交互作用和每个导联的局部动态特征。为进一步缓解模型过拟合，我们利用一个包含公开心电图-报告数据集的大型语言模型（LLM），进行心电图-报告对齐任务的预训练。随后，该网络基于英国生物银行研究中的两个特定队列（聚焦于高血压患者（UKB-HYP）和心肌梗死患者（UKB-MI））进行微调，用于心力衰竭风险预测。结果表明，基于LLM的预训练显著增强了这些队列中的心力衰竭风险预测性能。双注意力设计不仅提高了可解释性，还提升了预测准确性，在UKB-HYP和UKB-MI队列中分别取得了0.6349和0.5805的C指数得分，优于现有竞争方法。这证明了我们的方法在处理临床复杂心电图数据以推进心力衰竭风险评估方面的潜力。