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Collaborative learning of common latent representations in routinely collected multivariate ICU physiological signals
In Intensive Care Units (ICU), the abundance of multivariate time series presents an opportunity for machine learning (ML) to enhance patient phenotyping.
VAE-IF: Deep feature extraction with averaging for unsupervised artifact detection in routine acquired ICU time-series
Our approach offers a promising solution for cleaning ICU data in clinical research and practice without the need for any labels whatsoever.
Hybrid Artifact Detection System for Minute Resolution Blood Pressure Signals from ICU
Manual annotation by experienced researchers, which is the gold standard for removing artifacts, is time-consuming and costly due to the volume of the data generated in the ICU.
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