| Title: | Large Language Model-Informed ECG Dual Attention Network for Heart Failure Risk Prediction |
| Journal: | IEEE Transactions on Big Data |
| Published: | 30 Jan 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40524840/ |
| DOI: | https://doi.org/10.1109/tbdata.2025.3536922 |
Publication 14963
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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 12 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.</p>
6 Authors
- Chen Chen
- Lei Li
- Marcel Beetz
- Abhirup Banerjee
- Ramneek Gupta
- Vicente Grau
Enabling scientific discoveries that improve human health