Artificial intelligence-based ECG reconstruction error as a continuous predictor of all-cause mortality: a multi-cohort retrospective validation study

Abstract excerpt

<h4>Background</h4> Recent artificial intelligence (AI) models applied to the electrocardiogram (ECG) for risk stratification typically rely on supervised learning, defining risk as the error relative to an external target such as age or sex. This couples the risk score to the choice of target rather than the cardiac signal alone, and may limit generalisability. We aimed to develop a self-supervised AI-ECG risk s...

Topics

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• Algorithms
• Arrhythmias, Cardiac
• Artificial Intelligence
• Clinical Decision-Making
• Death, Sudden, Cardiac
• Genetic Testing
• Machine Learning
• Phenotype

Identifiers and source

Literature Corpus work

d74c3184-223c-5a81-9c35-4e18d7e687a7

DOI

10.64898/2026.07.10.26357749