Temporal patterns in insulin needs for Type 1 diabetes

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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 languageEnglish
PublisherNeurIPS 2022 Workshop on Learning from Time Series for Health
Publication statusPublished - 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/

Structured keywords

  • Interactive Artificial Intelligence CDT


  • cs.LG
  • q-bio.QM
  • stat.ML


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