ArticleEuropean heart journal. Digital health2023
Development and validation of a dynamic deep learning algorithm using electrocardiogram to predict dyskalaemias in patients with multiple visits.
Article in European heart journal. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Review
- Deep learning analysis of 12-lead electrocardiograms for bloodstream infection prediction: a multi-center validation study.BMC medical informatics and decision making · 2026Article
- Non-invasive hemoglobin estimation with outcome prediction via deep learning analysis of ECG-derived cardiac micro-dynamics.Internal and emergency medicine · 2026Article
- Development of deep learning algorithm for detecting dyskalemia based on electrocardiogram.Scientific reports · 2024Article
- Current and Future Use of Artificial Intelligence in Electrocardiography.Journal of cardiovascular development and disease · 2023Review
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6 authors.
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Abstract
Aims: Deep learning models (DLMs) have shown superiority in electrocardiogram (ECG) analysis and have been applied to diagnose dyskalaemias. However, no study has explored the performance of DLM-enabled ECG in continuous follow-up scenarios. Therefore, we proposed a dynamic revision of DLM-enabled ECG to use personal pre-annotated ECGs to enhance the accuracy in patients with multiple visits. Methods and results: We retrospectively collected 168 450 ECGs with corresponding serum potassium (K Conclusion: Our method has shown a distinguishable improvement in DLMs for diagnosing dyskalaemias in patients with multiple visits, and we also proved its application in ejection fraction prediction, which could further improve daily clinical practice.
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