  Blood glucose simulation allows the effectiveness of type 1 diabetes (T1D)
management strategies to be evaluated without patient harm. Deep learning
algorithms provide a promising avenue for extending simulator capabilities;
however, these algorithms are limited in that they do not necessarily learn
physiologically correct glucose dynamics and can learn incorrect and
potentially dangerous relationships from confounders in training data. This is
likely to be more important in real-world scenarios, as data is not collected
under strict research protocol. This work explores the implications of using
deep learning algorithms trained on real-world data to model glucose dynamics.
Free-living data was processed from the OpenAPS Data Commons and supplemented
with patient-reported tags of challenging diabetes events, constituting one of
the most detailed real-world T1D datasets. This dataset was used to train and
evaluate state-of-the-art glucose simulators, comparing their prediction error
across safety critical scenarios and assessing the physiological
appropriateness of the learned dynamics using Shapley Additive Explanations
(SHAP). While deep learning prediction accuracy surpassed the widely-used
mathematical simulator approach, the model deteriorated in safety critical
scenarios and struggled to leverage self-reported meal and exercise
information. SHAP value analysis also indicated the model had fundamentally
confused the roles of insulin and carbohydrates, which is one of the most basic
T1D management principles. This work highlights the importance of considering
physiological appropriateness when using deep learning to model real-world
systems in T1D and healthcare more broadly, and provides recommendations for
building models that are robust to real-world data constraints.
