Conference paper
In silico comparison of continuous glucose monitor failure mode strategies for an artificial pancreas
Proceedings of 5th International Workshop on Knowledge Discovery in Healthcare Data (KDH 2020) , Vol.2675, pp.53-57
International Workshop on Knowledge Discovery in Healthcare Data (KDH), 5th (Santiago de Compostela, Spain, 29-Aug-2020–30-Aug-2020)
CEUR Workshop Proceedings
2020
Abstract
An artificial pancreas is a medical Internet of Thingsbased system consisting of a continuous glucose monitor, an insulin pump, and a micro-controller. The use of artificial pancreas systems is becoming increasingly popular amongst patients with type 1 diabetes due to its effective ability to allow the patient better control of his/her own blood glucose levels compared to other more standard treatments. In this paper, the problem of missing sensor readings in the glucose monitor data is considered. How should the microcontroller (which adjusts the insulin pump based on monitor readings) react when the glucose monitor stops transmitting for an unpredictable period of time? A strategy that answers this question is called a failure mode strategy. In this paper, several potential failure mode strategies are explored in the context of simulation experiments. Results are presented showing that at least one effective and simple failure mode strategy (0.5µ & LR<72) exists.
Details
- Title
- In silico comparison of continuous glucose monitor failure mode strategies for an artificial pancreas
- Authors
- Yunjie (Lisa) Lu - University of WaikatoAbigail Koay - University of WaikatoMichael Mayo - University of Waikato
- Publication details
- Proceedings of 5th International Workshop on Knowledge Discovery in Healthcare Data (KDH 2020) , Vol.2675, pp.53-57
- Conference details
- International Workshop on Knowledge Discovery in Healthcare Data (KDH), 5th (Santiago de Compostela, Spain, 29-Aug-2020–30-Aug-2020)
- Publisher
- CEUR Workshop Proceedings
- Date published
- 2020
- Handle
- 10289/13870
- Organisation Unit
- Healthy Ageing Research Cluster; School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991230229902621
- Output Type
- Conference paper
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