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

Area of Science:

• Cardiology
• Artificial Intelligence
• Biomedical Engineering

Background:

• Heart failure (HF) presents a growing global health concern with increasing mortality rates.
• Early detection and prevention strategies are crucial for mitigating the impact of HF.
• Predicting HF risk from electrocardiograms (ECGs) is challenging due to complex data and imbalanced risk groups.

Purpose of the Study:

• To develop and validate a novel deep learning model for predicting heart failure (HF) risk using 12-lead ECGs.
• To enhance the model's performance by incorporating large language model (LLM) pre-training and a dual attention mechanism.
• To assess the model's effectiveness in specific patient cohorts, including those with hypertension and myocardial infarction.

Main Methods:

• A lightweight dual attention ECG network was designed with cross-lead and lead-specific temporal attention modules.
• The network utilized LLM pre-training on an ECG-Report dataset for improved feature extraction and to mitigate overfitting.
• The model was fine-tuned and evaluated on two UK Biobank cohorts: patients with hypertension (UKB-HYP) and myocardial infarction (UKB-MI).

Main Results:

• LLM-informed pre-training significantly improved HF risk prediction accuracy in both UKB-HYP and UKB-MI cohorts.
• The dual attention network achieved superior predictive performance compared to existing methods, with C-index scores of 0.6349 (UKB-HYP) and 0.5805 (UKB-MI).
• The attention mechanisms provided enhanced interpretability of the ECG features contributing to risk prediction.

Conclusions:

• The proposed LLM-enhanced dual attention ECG network offers a promising approach for early and accurate heart failure risk prediction.
• This methodology demonstrates potential for advancing clinical risk assessment using complex ECG data, especially in high-risk patient populations.
• The findings highlight the value of integrating advanced AI techniques, like LLMs and attention networks, in cardiovascular disease management.