ArticleEuropean heart journal. Digital health2022
Variational auto-encoders improve explainability over currently employed heatmap methods for deep learning-based interpretation of the electrocardiogram.
Article in European heart journal. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Synthetic artificial intelligence in cardiology: from generative models to clinical applications.European heart journal open · 2026Review
- Preoperative risk prediction of major cardiovascular events in noncardiac surgery using the 12-lead electrocardiogram: an explainable deep learning approach.British journal of anaesthesia · 2025Observational
- Enhancing explainability in clinical deep-learning models: Latent-space variable decoding is superior to gradient-weighted class activation mapping.Heart rhythm O2 · 2025Article
- Immune-related genes can accurately predict survival in bladder cancer: a retrospective study via two independent immunotherapy cohorts.Translational andrology and urology · 2025Article
- Quest for the ideal assessment of electrical ventricular dyssynchrony in cardiac resynchronization therapy.Journal of molecular and cellular cardiology plus · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.