Multi-Task Disentangled Autoencoder for Time-Series Data in Glucose Dynamics
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DOI: 10.1109/jbhi.2022.3175928
Abstract: The objective of this study is to propose MD-VAE: a multi-task disentangled variational autoencoders (VAE) for exploring characteristics of latent representations (LR) and exploiting LR for diverse tasks including glucose forecasting, event detection, and temporal… read more here.
Keywords: cgm data; disentangled autoencoder; task disentangled; glucose dynamics ... See more keywords