A physiologically-constrained neural network digital twin framework for replicating glucose dynamics in type 1 diabetes
- 2 April 2026
- journal article
- research article
- Published by Springer Nature in Neural Computing and Applications
- Vol. 38 (7) , 241
Abstract
No abstract availableKeywords
All Related Versions
Funding Information
- Juvenile Diabetes Research Foundation United States of America (2-SRA-2022-1273-S-B)
This publication has 66 references indexed in Scilit:
- Recent advancements and applications of physics-informed machine learning in biomedical researchCurrent Opinion in Biomedical Engineering, 2025
- An individualized digital twin of a patient for transdermal fentanyl therapy for chronic pain managementDrug Delivery and Translational Research, 2023
- A simulator with realistic and challenging scenarios for virtual T1D patients undergoing CSII and MDI therapyJournal of Biomedical Informatics, 2022
- Combining physics-based and data-driven modeling in well construction: Hybrid fluid dynamics modelingJournal of Natural Gas Science and Engineering, 2022
- Continuous-time system identification with neural networks: Model structures and fitting criteriaEuropean Journal of Control, 2021
- Mapping data to virtual patients in type 1 diabetesControl Engineering Practice, 2020
- A systematic stochastic design strategy achieving an optimal tradeoff between peak BGL and probability of hypoglycaemic events for individuals having type 1 diabetes mellitusBiomedical Signal Processing and Control, 2020
- Simulation software for assessment of nonlinear and adaptive multivariable control algorithms: Glucose–insulin dynamics in Type 1 diabetesComputers & Chemical Engineering, 2019
- Hypoglycemia in Type 1 Diabetes MellitusEndocrinology and Metabolism Clinics of North America, 2010
- The role of socioeconomic status, depression, quality of life, and glycemic control in type 1 diabetes mellitusThe Journal of Pediatrics, 2006