Patient contrastive learning: A performant, expressive, and practical approach to electrocardiogram modeling

Patient contrastive learning: A performant, expressive, and practical approach to electrocardiogram modeling

<jats:p>Supervised machine learning applications in health care are often limited due to a scarcity of labeled training data. To mitigate the effect of small sample size, we introduce a pre-training approach, <jats:bold>P</jats:bold>atient <jats:bold>C</jats:bold>ontras...

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Bibliographic Details
Main Authors: Diamant, Nathaniel, Reinertsen, Erik, Song, Steven, Aguirre, Aaron D, Stultz, Collin M, Batra, Puneet
Other Authors: Massachusetts Institute of Technology. Research Laboratory of Electronics
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2022
Online Access:https://hdl.handle.net/1721.1/143901

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https://hdl.handle.net/1721.1/143901

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