  We develop a new model of insulin-glucose dynamics for forecasting blood
glucose in type 1 diabetics. We augment an existing biomedical model by
introducing time-varying dynamics driven by a machine learning sequence model.
Our model maintains a physiologically plausible inductive bias and clinically
interpretable parameters -- e.g., insulin sensitivity -- while inheriting the
flexibility of modern pattern recognition algorithms. Critical to modeling
success are the flexible, but structured representations of subject variability
with a sequence model. In contrast, less constrained models like the LSTM fail
to provide reliable or physiologically plausible forecasts. We conduct an
extensive empirical study. We show that allowing biomedical model dynamics to
vary in time improves forecasting at long time horizons, up to six hours, and
produces forecasts consistent with the physiological effects of insulin and
carbohydrates.
