Heart failure (HF) poses a significant public health challenge due to its
rising global mortality rate. Addressing this issue through early diagnosis and
prevention could significantly reduce the disease's impact. This work
introduces a methodology for HF risk prediction using clinically acquired
12-lead electrocardiograms (ECGs). We present a novel, lightweight
dual-attention ECG network designed to capture complex ECG features essential
for early HF prediction, despite the notable imbalance between low and
high-risk groups. The network features a cross-lead attention module and twelve
lead-specific temporal attention modules to capture cross-lead interactions and
local temporal dynamics within each lead. To prevent model overfitting from
limited training data, 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). Our findings show that
LLM-informed pretraining significantly improves the network's HF risk
prediction capability in these cohorts. Moreover, the dual-attention mechanism
enhances interpretability and predictive performance, ensuring a transparent
and reliable prediction process. The method outperforms existing models,
achieving average C-index scores of 0.6349 and 0.5805 on the UKB-HYP and UKB-MI
test sets, respectively. This performance demonstrates our approach's
effectiveness in managing complex clinical ECG data and its potential to
improve HF risk assessment across various populations.

用于心力衰竭风险预测的 ECG 网络模型基于 12 导联心电图数据，通过大型语言模型的预训练提高预测能力和透明度，为早期心力衰竭诊断和预防提供潜在的改进。