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

arXiv:2403.10581v1 Announce Type: cross
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 …

abstract acquired arxiv attention challenge cs.ai cs.cl cs.lg diagnosis disease eess.sp failure global health heart failure impact issue language language model large language large language model methodology mortality network prediction prevention public public health q-bio.qm rate reduce risk through type work