ReviewCurrent diabetes reports2017
Multivariable Adaptive Artificial Pancreas System in Type 1 Diabetes.
Review in Current diabetes reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
14 citing papers in PubMed.
- Safety and Feasibility Evaluation of Step Count Informed Meal Boluses in Type 1 Diabetes: A Pilot Study.Journal of diabetes science and technology · 2022Trial
- Separating insulin-mediated and non-insulin-mediated glucose uptake during and after aerobic exercise in type 1 diabetes.American journal of physiology. Endocrinology and metabolism · 2021Trial
- Fully Closed-Loop Multiple Model Probabilistic Predictive Controller Artificial Pancreas Performance in Adolescents and Adults in a Supervised Hotel Setting.Diabetes technology & therapeutics · 2018Trial
- Dosing Algorithms for Insulin Pumps.Diabetes spectrum : a publication of the American Diabetes Association · 2025Article
- Recent advances in the precision control strategy of artificial pancreas.Medical & biological engineering & computing · 2024Review
- Integrating Multiple Inputs Into an Artificial Pancreas System: Narrative Literature Review.JMIR diabetes · 2022Review
- Prior Informed Regularization of Recursively Updated Latent-Variables-Based Models with Missing Observations.Control engineering practice · 2021Article
- Dual-hormone artificial pancreas for management of type 1 diabetes: Recent progress and future directions.Artificial organs · 2021Review
- Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices.BMC bioinformatics · 2020Article
- Simulation Software for Assessment of Nonlinear and Adaptive Multivariable Control Algorithms: Glucose - Insulin Dynamics in Type 1 Diabetes.Computers & chemical engineering · 2019Article
- Automated Insulin Delivery Algorithms.Diabetes spectrum : a publication of the American Diabetes Association · 2019Article
- Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review.Biomedical engineering online · 2019Review
- Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis.Advances in bioinformatics · 2019Review
- Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System.Diabetes technology & therapeutics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
purpose of reviewThe review summarizes the current state of the artificial pancreas (AP) systems and introduces various new modules that should be included in future AP systems. RECENT
findingsA fully automated AP must be able to detect and mitigate the effects of meals, exercise, stress and sleep on blood glucose concentrations. This can only be achieved by using a multivariable approach that leverages information from wearable devices that provide real-time streaming data about various physiological variables that indicate imminent changes in blood glucose concentrations caused by meals, exercise, stress and sleep. The development of a fully automated AP will necessitate the design of multivariable and adaptive systems that use information from wearable devices in addition to glucose sensors and modify the models used in their model-predictive alarm and control systems to adapt to the changes in the metabolic state of the user. These AP systems will also integrate modules for controller performance assessment, fault detection and diagnosis, machine learning and classification to interpret various signals and achieve fault-tolerant control. Advances in wearable devices, computational power, and safe and secure communications are enabling the development of fully automated multivariable AP systems.
Indexed as
Identifiers
28812204What OpenQuestion holds
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.