Neighborhood Contrastive Learning Applied to Online Patient Monitoring
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2021-06-09
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Working Paper
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yes
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Abstract
Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monitoring is often formulated as a supervised learning problem. Recently, contrastive learning approaches have demonstrated promising improvements over competitive supervised benchmarks. These methods rely on well-understood data augmentation techniques developed for image data which do not apply to online monitoring. In this work, we overcome this limitation by supplementing time-series data augmentation techniques with a novel contrastive learning objective which we call neighborhood contrastive learning (NCL). Our objective explicitly groups together contiguous time segments from each patient while maintaining state-specific information. Our experiments demonstrate a marked improvement over existing work applying contrastive methods to medical time-series.
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published
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2106.05142
Publisher
Cornell University
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09568 - Rätsch, Gunnar / Rätsch, Gunnar
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176005 - Novel Machine Learning Approaches for Data from the Intensive Care Unit (SNF)
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