ArticleProceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control2014
Moving-horizon-like state estimation via continuous glucose monitor feedback in MPC of an artificial pancreas for type 1 diabetes.
Article in Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Velocity-weighting & velocity-penalty MPC of an artificial pancreas: Improved safety & performance.Automatica : the journal of IFAC, the International Federation of Automatic Control · 2018Article
- Event-Triggered Model Predictive Control for Embedded Artificial Pancreas Systems.IEEE transactions on bio-medical engineering · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
An extension of a novel state estimation scheme is presented. The proposed method is developed for model predictive control (MPC) of an artificial pancreas for automatic insulin delivery to people with type 1 diabetes mellitus; specifically, glycemia control based on feedback by a continuous glucose monitor. The state estimation strategy is akin to moving-horizon estimation, but effectively exploits knowledge of sensor recalibrations, ameliorates the effects of delays between measurements and the controller call, and accommodates irregularly sampled output measurements. The method performs a function fit and a sampling action to synthesize a mock output trajectory for constructing the state. In this paper the structure of the fitted function prototype is divorced from the structure of the function that is sampled, facilitating the strategic elimination of prediction artifacts that are not observed in the actual plant. The proposed estimation strategy is demonstrated using clinical data collected by a Dexcom G4 Platinum continuous glucose monitor.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.