Temporal patterns in insulin needs for Type 1 diabetes

Isabella Degen, Zahraa S. Abdallah

Keywords: Timeseries, insulin needs, Type 1 diabetes, multivariate Time Series, clustering

TL;DR: We leverage the OpenAPS Data Commons dataset to discover new temporal patterns in insulin need rather than well-known ones, such as where insulin need is changing when carbohydrates change using time series methods

Abstract: 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.

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/temporal-patterns-in-insulin-needs-for-type-1/code)