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Neighborhood Contrastive Learning Applied to Online Patient Monitoring
Hugo Yèche · Gideon Dresdner · Francesco Locatello · Matthias Hüser · Gunnar Rätsch

Thu Jul 22 05:30 PM -- 05:35 PM (PDT) @ None

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.

Author Information

Hugo Yèche (ETH Zürich)
Gideon Dresdner (ETH Zürich)
Francesco Locatello (ETH Zurich - Max Planck Institute)
Matthias Hüser (ETH Zürich)
Gunnar Rätsch (ETH Zurich)

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