  Type 1 Diabetes (T1D) is a chronic condition where the body produces little
or no insulin, a hormone required for the cells to use blood glucose (BG) for
energy and to regulate BG levels in the body. Finding the right insulin dose
and time remains a complex, challenging and as yet unsolved control task. In
this study, we use the OpenAPS Data Commons dataset, which is an extensive
dataset collected in real-life conditions, to discover temporal patterns in
insulin need driven by well-known factors such as carbohydrates as well as
potentially novel factors. We utilised various time series techniques to spot
such patterns using matrix profile and multi-variate clustering. The better we
understand T1D and the factors impacting insulin needs, the more we can
contribute to building data-driven technology for T1D treatments.
