ArticleiScience2026
FPECGNET: A deep learning framework based on federated learning and prototype learning for interpretable ECG classification with privacy.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Under everyday operational conditions, data privacy regulations prohibit sharing information between hospitals or institutions, contributing to the emergence of data silos. This scarcity of data hinders improvements in model performance. Another major obstacle to AI applications in medicine is the lack of interpretability, where the decision-making process of models cannot be reflected. We propose a deep learning framework based on federated learning and prototype learning that simulates the reality of hospital data silos while endowing the model with interpretability. Using the publicly available PTB-XL dataset, we divided it into three subsets by category and trained the model using the federated learning framework, achieving superior performance on these subsets. When tested on the aggregated model using the PTB-XL dataset, it demonstrated performance comparable to the current state-of-the-art model.
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What Socratic holds
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 Socratic graph.