Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction
<|reference_start|>Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction: 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.<|reference_end|>
@article{chen2024large,
    title={Large Language Model-informed ECG Dual Attention Network for Heart
  Failure Risk Prediction},
    author={Chen Chen, Lei Li, Marcel Beetz, Abhirup Banerjee, Ramneek Gupta,
  Vicente Grau},
    journal={arXiv preprint arXiv:2403.10581},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10581},
    primaryClass={q-bio.QM cs.AI cs.CL cs.LG eess.SP}
}