Personalized Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes

Key points

• Accurate, physiologically consistent glucose forecasts could help people with type 1 diabetes respond to impending excursions.1
• A temporal convolutional network (TCN) used 180 min of history, constrained during training by a personalized four-parameter physiological model.1
• The main PMB--NN combined forecast and neural--physiological agreement losses; a three-term variant additionally fitted physiological predictions to measured glucose.1
• PMB--NN combines personalized forecasting with an explicit physiological model, allowing assessment of predictive accuracy, physiological response behavior, and participant-specific coefficients.1

Sources (1)

• [1]Personalized Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 08:57 AM

  We applied our physiological-model-based neural network (PMB--NN) framework to glucose forecasting.

  Accurate, physiologically consistent glucose forecasts could help people with type 1 diabetes respond to impending excursions.

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