Cardiovascular diseases, including Heart Failure (HF), remain a leading
global cause of mortality, often evading early detection. In this context,
accessible and effective risk assessment is indispensable. Traditional
approaches rely on resource-intensive diagnostic tests, typically administered
after the onset of symptoms. The widespread availability of electrocardiogram
(ECG) technology and the power of Machine Learning are emerging as viable
alternatives within smart healthcare. In this paper, we propose several
multi-modal approaches that combine 30-second ECG recordings and approximate
long-term Heart Rate Variability (HRV) data to estimate the risk of HF
hospitalization. We introduce two survival models: an XGBoost model with
Accelerated Failure Time (AFT) incorporating comprehensive ECG features and a
ResNet model that learns from the raw ECG. We extend these with our novel
long-term HRVs extracted from the combination of ultra-short-term beat-to-beat
measurements taken over the day. To capture their temporal dynamics, we propose
a survival model comprising ResNet and Transformer architectures (TFM-ResNet).
Our experiments demonstrate high model performance for HF risk assessment with
a concordance index of 0.8537 compared to 14 survival models and competitive
discrimination power on various external ECG datasets. After transferability
tests with Apple Watch data, our approach implemented in the myHeartScore App
offers cost-effective and highly accessible HF risk assessment, contributing to
its prevention and management.

该研究论文提出了多模式方法，结合 30 秒的心电图记录和近似的长期心率变异性数据（HRV）来估计心衰住院风险。实验结果显示，在心衰风险评估方面，所提出的方法具有较高的模型性能，并在多个外部心电图数据集上展示了竞争性的区分力。通过在 Apple Watch 数据上的可迁移性测试，并在 myHeartScore 应用程序中实施该方法，能够提供成本效益高且易于获取的心衰风险评估，有助于心衰的预防和管理。