Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction
CoRR(2024)
Abstract
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.
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