ArticleJournal of diabetes science and technology2018
Adaptive and Personalized Plasma Insulin Concentration Estimation for Artificial Pancreas Systems.
Article in Journal of diabetes science and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The trial behind it
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Who cites it
19 citing papers in PubMed, 44 citations in OpenAlex.
- Metabolic Models, in Silico Trials, and Algorithms.Diabetes technology & therapeutics · 2025Review
- Metabolic Models, in Silico Trials, and Algorithms.Journal of diabetes science and technology · 2025Review
- Multivariable Automated Insulin Delivery System for Handling Planned and Spontaneous Physical Activities.Journal of diabetes science and technology · 2023Article
- Review
- Meal and Physical Activity Detection from Free-living Data for Discovering Disturbance Patterns to Glucose Levels in People with Diabetes.BioMedInformatics · 2022Article
- Quantifying the impact of physical activity on future glucose trends using machine learning.iScience · 2022Article
- Incorporating Prior Information in Adaptive Model Predictive Control for Multivariable Artificial Pancreas Systems.Journal of diabetes science and technology · 2022Article
- Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions.Sensors (Basel, Switzerland) · 2021Article
- Learning Carbohydrate Digestion and Insulin Absorption Curves Using Blood Glucose Level Prediction and Deep Learning Models.Sensors (Basel, Switzerland) · 2021Article
- Physical Activity and Psychological Stress Detection and Assessment of Their Effects on Glucose Concentration Predictions in Diabetes Management.IEEE transactions on bio-medical engineering · 2021Article
- Discrimination of simultaneous psychological and physical stressors using wristband biosignals.Computer methods and programs in biomedicine · 2021Article
- Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System.Journal of healthcare engineering · 2020Article
- Replacing Pumps with Light Controlled Insulin Delivery.Current diabetes reports · 2019Review
- Simulation Software for Assessment of Nonlinear and Adaptive Multivariable Control Algorithms: Glucose - Insulin Dynamics in Type 1 Diabetes.Computers & chemical engineering · 2019Article
- Controlling the AP Controller: Controller Performance Assessment and Modification.Journal of diabetes science and technology · 2019Article
- Automated Insulin Delivery Algorithms.Diabetes spectrum : a publication of the American Diabetes Association · 2019Article
- Plasma-Insulin-Cognizant Adaptive Model Predictive Control for Artificial Pancreas Systems.Journal of process control · 2019Article
- Toward Better Understanding of Insulin Therapy by Translation of a PK-PD Model to Visualize Insulin and Glucose Action Profiles.Journal of clinical pharmacology · 2019Article
- Incorporating Unannounced Meals and Exercise in Adaptive Learning of Personalized Models for Multivariable Artificial Pancreas Systems.Journal of diabetes science and technology · 2018Article
Corrections and comments
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Authors and funding
14 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundThe artificial pancreas (AP) system, a technology that automatically administers exogenous insulin in people with type 1 diabetes mellitus (T1DM) to regulate their blood glucose concentrations, necessitates the estimation of the amount of active insulin already present in the body to avoid overdosing.
methodAn adaptive and personalized plasma insulin concentration (PIC) estimator is designed in this work to accurately quantify the insulin present in the bloodstream. The proposed PIC estimation approach incorporates Hovorka's glucose-insulin model with the unscented Kalman filtering algorithm. Methods for the personalized initialization of the time-varying model parameters to individual patients for improved estimator convergence are developed. Data from 20 three-days-long closed-loop clinical experiments conducted involving subjects with T1DM are used to evaluate the proposed PIC estimation approach.
resultsThe proposed methods are applied to the clinical data containing significant disturbances, such as unannounced meals and exercise, and the results demonstrate the accurate real-time estimation of the PIC with the root mean square error of 7.15 and 9.25 mU/L for the optimization-based fitted parameters and partial least squares regression-based testing parameters, respectively.
conclusionsThe accurate real-time estimation of PIC will benefit the AP systems by preventing overdelivery of insulin when significant insulin is present in the bloodstream.
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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.