ArticleControl engineering practice2018
Model-Fusion-Based Online Glucose Concentration Predictions in People with Type 1 Diabetes.
Article in Control engineering practice, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed, 39 citations in OpenAlex.
- Future-aware blood glucose forecasting using knowledge distillation with transformer-based sequence-to-sequence models.Scientific reports · 2026Article
- Recent advances in the precision control strategy of artificial pancreas.Medical & biological engineering & computing · 2024Review
- Article
- Robust positive control of tumour growth using angiogenic inhibition.IET systems biology · 2023Article
- Positive input observer-based controller design for blood glucose regulation for type 1 diabetic patients: A backstepping approach.IET systems biology · 2022Article
- GLYFE: review and benchmark of personalized glucose predictive models in type 1 diabetes.Medical & biological engineering & computing · 2022Review
- Prior Informed Regularization of Recursively Updated Latent-Variables-Based Models with Missing Observations.Control engineering practice · 2021Article
- Short-term prediction of glucose in type 1 diabetes using kernel adaptive filters.Medical & biological engineering & computing · 2019Observational
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
Abstract
Accurate predictions of glucose concentrations are necessary to develop an artificial pancreas (AP) system for people with type 1 diabetes (T1D). In this work, a novel glucose forecasting paradigm based on a model fusion strategy is developed to accurately characterize the variability and transient dynamics of glycemic measurements. To this end, four different adaptive filters and a fusion mechanism are proposed for use in the online prediction of future glucose trajectories. The filter fusion mechanism is developed based on various prediction performance indexes to guide the overall output of the forecasting paradigm. The efficiency of the proposed model fusion based forecasting method is evaluated using simulated and clinical datasets, and the results demonstrate the capability and prediction accuracy of the data-based fusion filters, especially in the case of limited data availability. The model fusion framework may be used in the development of an AP system for glucose regulation in patients with T1D.
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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.