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Martin Ubl
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2020 – today
- 2023
- [j3]Antonio Della Cioppa, Ivan De Falco, Tomas Koutny, Umberto Scafuri
, Martin Ubl, Ernesto Tarantino:
Reducing high-risk glucose forecasting errors by evolving interpretable models for Type 1 diabetes. Appl. Soft Comput. 134: 110012 (2023) - [j2]Ivanoe De Falco
A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction. Sensors 23(6): 2957 (2023) - 2022
- [j1]Martin Ubl
Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS.R for Diabetes Treatment. Sensors 22(23): 9445 (2022) - [c7]Martin Ubl, Tomas Koutny:
A Novel Approach to Multi-Compartmental Model Implementation to Achieve Metabolic Model Identifiability on Patient's CGM Data. EUSPN/ICTH 2022: 116-123 - [c6]Ivanoe De Falco, Antonio Della Cioppa, Tomas Koutny, Umberto Scafuri
An Evolution-based Machine Learning Approach for Inducing Glucose Prediction Models. ISCC 2022: 1-6 - 2021
- [c5]Ivanoe De Falco, Antonio Della Cioppa, Tomas Koutny, Umberto Scafuri
Grammatical Evolution-Based Approach for Extracting Interpretable Glucose-Dynamics Models. ISCC 2021: 1-6 - 2020
- [c4]Tomas Koutny, Martin Ubl
SmartCGMS as a Testbed for a Blood-Glucose Level Prediction and/or Control Challenge with (an FDA-Accepted) Diabetic Patient Simulation. EUSPN/ICTH 2020: 354-362
2010 – 2019
- 2019
- [c3]Martin Ubl
SmartCGMS as an Environment for an Insulin-Pump Development with FDA-Accepted In-Silico Pre-Clinical Trials. EUSPN/ICTH 2019: 322-329 - [c2]Tomas Koutny, Martin Ubl
Comparing the PaGMO Framework to a De-randomized Meta-Differential Evolution on Calculation and Prediction of Glucose Levels. ISCC 2019: 1056-1061 - 2018
- [c1]Tomas Koutny, Martin Ubl
Parallel software architecture for the next generation of glucose monitoring. EUSPN/ICTH 2018: 279-286
Coauthor Index
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