  Patients with Type I Diabetes (T1D) must take insulin injections to prevent
the serious long term effects of hyperglycemia - high blood glucose (BG).
Patients must also be careful not to inject too much insulin because this could
induce hypoglycemia (low BG), which can potentially be fatal. Patients
therefore follow a "regimen" that determines how much insulin to inject at
certain times. Current methods for managing this disease require adjusting the
patient's regimen over time based on the disease's behavior (recorded in the
patient's diabetes diary). If we can accurately predict a patient's future BG
values from his/her current features (e.g., predicting today's lunch BG value
given today's diabetes diary entry for breakfast, including insulin injections,
and perhaps earlier entries), then it is relatively easy to produce an
effective regimen. This study explores the challenges of BG modeling by
applying several machine learning algorithms and various data preprocessing
variations (corresponding to 312 [learner, preprocessed-dataset] combinations),
to a new T1D dataset containing 29 601 entries from 47 different patients. Our
most accurate predictor is a weighted ensemble of two Gaussian Process
Regression models, which achieved an errL1 loss of 2.70 mmol/L (48.65 mg/dl).
This was an unexpectedly poor result given that one can obtain an errL1 of 2.91
mmol/L (52.43 mg/dl) using the naive approach of simply predicting the
patient's average BG. For each of data-variant/model combination we report
several evaluation metrics, including glucose-specific metrics, and find
similarly disappointing results (the best model was only incrementally better
than the simplest measure). These results suggest that the diabetes diary data
that is typically collected may not be sufficient to produce accurate BG
prediction models; additional data may be necessary to build accurate BG
prediction models.
