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Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation.

Louis A GomezAdedolapo Aishat ToyeR Stanley HumSamantha Kleinberg
Published in: Journal of diabetes science and technology (2023)
We find a significant gap between BG forecasting performance on simulated and real data, and our method can be used to close this gap. This will enable researchers to rigorously test algorithms and provide realistic estimates of real-world performance without overfitting to real data or at the expense of data collection.
Keyphrases
  • electronic health record
  • big data
  • machine learning
  • type diabetes
  • deep learning
  • metabolic syndrome
  • skeletal muscle