Background Artificial intelligence ECG (AI-ECG) models can predict cardiovascular outcomes, but their clinical adoption is limited by restricted access to training data and uncertain generalisability.
Abstract: Despite advancements using graph neural networks (GNNs) to capture complex user-item interactions, challenges persist due to data sparsity and noise. To address these, self-supervised ...
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