A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions
Deep learning algorithms trained on data streamed temporally from different clinical sites and from a multitude of physiological sensors are generally affected by a degradation in performance. To mitigate this, the authors propose a continual learning strategy that employs a replay buffer.
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Main Authors: | , , |
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Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2021
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Subjects: | |
Online Access: | https://doaj.org/article/5943e24bee1a4eb1be3eaa04425d47c2 |
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Summary: | Deep learning algorithms trained on data streamed temporally from different clinical sites and from a multitude of physiological sensors are generally affected by a degradation in performance. To mitigate this, the authors propose a continual learning strategy that employs a replay buffer. |
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