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Neighborhood Contrastive Learning Applied To Online Patient Monitoring by xiao.zhao.atwork is a document available to read on EtoBox.

This document presents a novel contrastive learning framework called Neighborhood Contrastive Learning (NCL) aimed at improving online patient monitoring in intensive care units. NCL addresses challenges associated with heterogeneous medical time-series data by grouping contiguous time segments from patients while preserving state-specific information. The approach demonstrates competitive results in online monitoring benchmarks and outperforms existing supervised learning methods, particularly in limited l

Author
xiao.zhao.atwork
Language
EN