Individuals with Type I diabetes (T1D) must frequently monitor their blood glucose (BG) and deliver insulin to regulate it. New devices like continuous glucose monitors (CGMs) and insulin pumps  have  helped  reduce  this  burden  by  facilitating  closed-loop technologies like the artificial pancreas (AP) for delivering insulin automatically. As more people use AP systems, which rely on a CGM and insulin pump, there has been a dramatic increase in the availability of large scale patient-generated health data (PGHD) in T1D. This data can potentially be used to train robust, generalizable models for accurate BG forecasting which can then be used to make forecasts for smaller datasets like OhioT1DM in real-time. In this work, we investigate the potential and pitfalls of using knowledge distillation to transfer knowledge from a model learned from one dataset to another and compare it with the baseline case of using either dataset alone. We  show  that  using  a  pre-trained  model  to  do  BG  forecasting  for OhioT1DM from CGM data only (univariate setting) has comparable performance to training on OhioT1DM itself. Using a single-step, univariate  recurrent  neural  network  (RNN)  trained  on  OhioT1DM data alone, we achieve an overall RMSE of 19.21 and 31.77 mg/dl for a prediction horizon (PH) of 30 and 60 minutes respectively

Investigating potentials and pitfalls of knowledge distillation across datasets for blood glucose forecasting

The 5th International Workshop on Knowledge Discovery in Healthcare Data (KDH 2020)

technical paper

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ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Hadia Hameed

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Investigating potentials and pitfalls of knowledge distillation across datasets for blood glucose forecasting

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