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.
| Original language | English |
|---|---|
| Publisher | NeurIPS 2022 Workshop on Learning from Time Series for Health |
| DOIs | |
| Publication status | Published - 14 Nov 2022 |
Bibliographical note
Submitted and accepted for presentation as a poster at the NeurIPS22 Time series for Health workshop, https://timeseriesforhealth.github.io/Research Groups and Themes
- Interactive Artificial Intelligence CDT
Keywords
- cs.LG
- q-bio.QM
- stat.ML
Access to Document
- Full-text PDF (pre-print)
This is a pre-print version of the article (version of record). It first appeared online via arXiv at https://doi.org/10.48550/arXiv.2211.07393. Please refer to any applicable terms of use of the publisher.