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Deep learning model using ECGs during sleep studies can predict cardiovascular outcomes
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A research team supported by the National Institutes of Health (NIH) has found that electrocardiograms (ECGs) administered during sleep and paired with sleep stage information can be used to determine the risk of future adverse cardiac events. The study, which used a deep learning approach, suggests that using ECGs to predict which patients have a greater risk of poor heart-related outcomes could help guide clinical decision-making. The findings were published in the journal SLEEP.
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