| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A Comparative Study of Transformer-Based Models for Multi-Horizon Blood Glucose Prediction ⁎ ⁎ Work funded by National Institute Health (NIH) grant 1R01DK133148.
|
Karagoz, Meryem Altin
|
|
|
59 |
2 |
p. 155-160
|
artikel
|
| 2 |
A Deep Deterministic Policy Gradient control algorithm for Automatic Insulin Delivery
|
Lops, Giada
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|
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59 |
2 |
p. 13-18
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artikel
|
| 3 |
Aligning Insulin Therapy with Individual Preferences: A Multi-objective Decision Support System
|
Pryor, Elliott C.
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|
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59 |
2 |
p. 167-172
|
artikel
|
| 4 |
An AI-enabled dual-hormone model predictive control algorithm that delivers insulin and pramlintide
|
Jacobs, Peter G.
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|
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59 |
2 |
p. 61-66
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artikel
|
| 5 |
An Algorithm for Retrospectively Detecting Missing Meal Records in Type 1 Diabetes Datasets
|
Pavan, Jacopo
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|
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59 |
2 |
p. 85-90
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artikel
|
| 6 |
Analysis of commonly used meal models for type 1 diabetes from an identification perspective
|
Clara, Furió-Novejarque
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|
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59 |
2 |
p. 97-102
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artikel
|
| 7 |
An Open-Source Browser-Based Nonlinear Model Predictive Controller for Type 1 Diabetes
|
Hauser, Lara
|
|
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59 |
2 |
p. 143-148
|
artikel
|
| 8 |
Automated Insulin Delivery Systems for People with Type 2 Diabetes
|
Arentsen, Mathilde Guldbæk
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|
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59 |
2 |
p. 7-12
|
artikel
|
| 9 |
Comparing individualization strategies of Model Predictive Control for Artificial Pancreas
|
Cester, Lorenzo
|
|
|
59 |
2 |
p. 37-42
|
artikel
|
| 10 |
Comprehensive Analysis of Convex Hull Manipulation in the Context of Identifiable Virtual Patient Model Control
|
Varga, Árpád
|
|
|
59 |
2 |
p. 115-120
|
artikel
|
| 11 |
Contents
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|
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59 |
2 |
p. i-vii
|
artikel
|
| 12 |
Data-Driven Anticipation of Meal Intakes for Automated Insulin Delivery Systems with Model Predictive Control Technology ⁎ ⁎ This work was supported by National Institutes of Health (NIH) grants R01DK129553, R01DK133148.
|
Castillo, A.
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59 |
2 |
p. 55-60
|
artikel
|
| 13 |
Development of an advanced continuous glucose monitoring system using ESP32
|
Kubascik, M.
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59 |
2 |
p. 139-142
|
artikel
|
| 14 |
Extrapolation of Neural Networks for On-Chip Model Predictive Control: Insights from Nearest Neighbor Filtered Dataset and Disturbance Analysis
|
Shen, Jiaxin(Olivia)
|
|
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59 |
2 |
p. 67-72
|
artikel
|
| 15 |
Hybrid Modeling of the Insulin-Glucose System: Combining Linear and Data-Driven Models for Artificial Pancreas
|
Sonzogni, Beatrice
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|
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59 |
2 |
p. 109-114
|
artikel
|
| 16 |
In-silico Assessment of Using Faster Insulin Analogs in Automated Insulin Delivery (AID) Systems Without Meal Announcement ⁎ ⁎ This work has been supported by NIH under grant R01-DK-129553 and Breakthrough T1D under grant 2-APF-2024-1494-A-N
|
Moscoso-Vasquez, Marcela
|
|
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59 |
2 |
p. 19-24
|
artikel
|
| 17 |
In-Silico Validation of Parameter Optimization Strategies for Automated Insulin Delivery Systems using the UVA Replay Simulation Technology
|
Villa-Tamayo, María F.
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|
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59 |
2 |
p. 127-132
|
artikel
|
| 18 |
Integrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling ⁎ ⁎ This work was funded by the National Plan for NRRP Complementary Investments (PNC, established with the decree-law 6 May 2021, n. 59, converted by law n. 101 of 2021) in the call for the funding of research initiatives for technologies and innovative trajectories in the health and care sectors (Directorial Decree n. 931 of 06-06-2022) - project n. PNC0000003 - AdvaNced Technologies for Human-centrEd Medicine (project acronym: ANTHEM).
|
De Carli, S.
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|
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59 |
2 |
p. 91-96
|
artikel
|
| 19 |
Managing Blood Glucose in Premature Neonates via Parenteral Nutrition: In-silico Evaluation ⁎ ⁎ This work has been partially funded by the European Union’s Horizon Europe research and innovation programme (101099093), by the Agency for the Management of University and Research Grants AGAUR (BP 00137/2022) and by the National Council of Scientific and Technological Development, CNPq - Brazil (200007/2025-4).
|
Bertachi, Arthur
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59 |
2 |
p. 1-6
|
artikel
|
| 20 |
Meal Detection and Carbohydrates Counting for In-Silico Type 1 Diabetic Patients Based on Supervised Learning ⁎ ⁎ Universidad Industrial de Santander.
|
Edward-Alfonso, Rodriguez
|
|
|
59 |
2 |
p. 79-84
|
artikel
|
| 21 |
Multiple Constrained MPCs for glucose regulation in Type 1 Diabetes 1 1 This work was supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101115233 (MuSiC4Diabetes project) and by the European Union – NextGenerationEU – Project “Adaptive Personalised Safe Artificial Pancreas for children and adolescents (APS-AP)” – CUP F53D23000720006 - Grant Assignment Decree No. 960 adopted on 30/06/2023 by the Italian Ministry of University and Research (MUR).
|
Ragni, Matteo
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59 |
2 |
p. 31-36
|
artikel
|
| 22 |
Online Meal Detection Based on CGM Data Dynamics
|
Tavasoli, Ali
|
|
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59 |
2 |
p. 73-78
|
artikel
|
| 23 |
Parameters Relevance of a Glucose-Insulin Model in Type 1 Diabetes is Dependent on Meal Behavior
|
Escorihuela-Altaba, Clara
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59 |
2 |
p. 121-126
|
artikel
|
| 24 |
Periodic MPC for glucose control in Type 1 Diabetes 1 1 This work was supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101115233 (MuSiC4Diabetes project) and by the European Union – NextGenerationEU – Project “Adaptive Personalised Safe Artificial Pancreas for children and adolescents (APS-AP)” – CUP F53D23000720006 - Grant Assignment Decree No. 960 adopted on 30/06/2023 by the Italian Ministry of University and Research (MUR).
|
Mongini, Paolo A.
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59 |
2 |
p. 25-30
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artikel
|
| 25 |
Personalized Meal Bolus Calculator for Type-1 Diabetes Accounting for Diurnal Effects
|
Krishnamoorthy, Dinesh
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|
|
59 |
2 |
p. 133-138
|
artikel
|
| 26 |
POGO: A Method for individualization of Automated Insulin Delivery Systems
|
Pryor, Elliott C.
|
|
|
59 |
2 |
p. 43-48
|
artikel
|
| 27 |
Robotics and gamified simulation for paediatric diabetes education: feasibility and satisfaction analysis
|
Juan-Fernando, Martín-SanJosé
|
|
|
59 |
2 |
p. 149-154
|
artikel
|
| 28 |
Simulation of High-Fat High-Protein Meals Using the UVA/Padova T1D Simulator
|
Faggionato, Edoardo
|
|
|
59 |
2 |
p. 103-108
|
artikel
|
| 29 |
Stochastic Model Predictive Control of Blood Glucose Levels using Probabilistic Meal Anticipation ⁎ ⁎ Financial support from the NIH with grants 1DP3DK101075, 1R01DK130049 and R01DK135116, is gratefully acknowledged.
|
Ahmadasas, Mohammad
|
|
|
59 |
2 |
p. 49-54
|
artikel
|
| 30 |
The Neonate Glucose Simulator: A New Tool for Testing a Nutritional Clinical Advisor to Regulate Glycemia in Preterm Infants admitted to the Neonatal Intensive Care Unit
|
Marchiori, Hadija
|
|
|
59 |
2 |
p. 161-166
|
artikel
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