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Jose F. Garcia-Tirado
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2020 – today
- 2024
- [c6]Aylin Tastan
, Clara Escorihuela-Altaba
Clustering Time Series Data for Personalized Type 1 Diabetes Management. ICPRAI (2) 2024: 196-211 - [c5]Fabian Hüni
LSTM Networks and Graph Neural Networks for Predicting Events of Hypoglycemia. S+SSPR 2024: 52-61 - 2022
- [j4]Carlos E. Builes-Montaño
Main glucose hepatic fluxes in healthy subjects predicted from a phenomenological-based model. Comput. Biol. Medicine 142: 105232 (2022) - [c4]Patricio Colmegna, Jenny L. Diaz C., Jose F. Garcia-Tirado, Marc D. Breton:
Enabling Anticipatory Response in Multi-Stage MPC Formulation for Fully Automated Artificial Pancreas System. CDC 2022: 2566-2571 - [c3]María F. Villa-Tamayo, Jenny L. Diaz C.
Model Personalization in an Advanced Automated Insulin Delivery System: An In-Silico Exploration. ICSC 2022: 588-593 - 2021
- [j3]Juan David Hoyos Giraldo
Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis. IEEE Access 9: 69173-69188 (2021) - [j2]Jose F. Garcia-Tirado
Advanced hybrid artificial pancreas system improves on unannounced meal response - In silico comparison to currently available system. Comput. Methods Programs Biomed. 211: 106401 (2021)
2010 – 2019
- 2019
- [c2]Jose F. Garcia-Tirado
Ensemble Model Predictive Control Strategies Can Reduce Exercise Hypoglycemia in Type 1 Diabetes: In Silico Studies. ACC 2019: 4752-4758 - 2018
- [j1]C. Zuluaga-Bedoya
A dynamical model of an aeration plant for wastewater treatment using a phenomenological based semi-physical modeling methodology. Comput. Chem. Eng. 117: 420-432 (2018) - [c1]Simone Faccioli
Black-box Model Identification of Physical Activity in Type-l Diabetes Patients. EMBC 2018: 3910-3913
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