Individualized Models for Glucose Prediction in Type 1 Diabetes: Comparing Black-Box Approaches to a Physiological White-Box One-Reference-Cited by-同舟云学术

Individualized Models for Glucose Prediction in Type 1 Diabetes: Comparing Black-Box Approaches to a Physiological White-Box One

Published:2023-11 Issue:11 Volume:70 Page:3105-3115
ISSN:0018-9294
Container-title:IEEE Transactions on Biomedical Engineering
language:
Short-container-title:IEEE Trans. Biomed. Eng.

Author:

Cappon Giacomo¹^ORCID,Prendin Francesco¹^ORCID,Facchinetti Andrea¹^ORCID,Sparacino Giovanni¹^ORCID,Favero Simone Del²^ORCID

Affiliation:

1. Department of Information Engineering, University of Padova, Italy

2. Department of Information Engineering, University of Padova, Padova, Italy

Funder

Italian Ministry of Education, Universities and Research through the Projects Learn4AP: Patient-Specific Models for an Adaptive and Fault-Tolerant Artificial Pancreas

SIR: Scientific Independence of young Researchers

GLUCOse Monitoring electronic System FOR Type 1 diabetes individuals through the initiative PRIN: Progetti di Rilevante Interesse Nazionale

Dipartimenti di Eccellenza

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/10/10288106/10128700.pdf?arnumber=10128700

Reference57 articles.

1. Glucose Concentration can be Predicted Ahead in Time From Continuous Glucose Monitoring Sensor Time-Series

2. Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models

3. Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes

4. Adaptive proposal distribution for random walk Metropolis algorithm

5. An ARIMA Model With Adaptive Orders for Predicting Blood Glucose Concentrations and Hypoglycemia

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1. Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App;Journal of Diabetes Science and Technology;2024-08-19

2. Nocturnal Hypoglycemia in the Era of Continuous Glucose Monitoring;Journal of Diabetes Science and Technology;2024-08-19

3. Real-Time Blood Glucose Prediction Reveals a Discrepancy Between Performance Metrics and Real-World Evaluations;2024 IEEE International Conference on Real-time Computing and Robotics (RCAR);2024-06-24

4. AWD-stacking: An enhanced ensemble learning model for predicting glucose levels;PLOS ONE;2024-02-14

5. Logical analysis of data using linear approximation and heuristic algorithms for gene expression-based diagnostics;ITM Web of Conferences;2024