Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction

• URL: http://arxiv.org/abs/2403.10581v2
• Date: Fri, 22 Mar 2024 16:00:24 GMT
• Title: Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction
• Authors: Chen Chen, Lei Li, Marcel Beetz, Abhirup Banerjee, Ramneek Gupta, Vicente Grau,
• Abstract summary: Heart failure (HF) poses a significant public health challenge, with a rising global mortality rate. 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.
• Score: 9.823423993036055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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