  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.
