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Reducing high-risk glucose forecasting errors by evolving interpretable models for Type 1 diabetes.

, , , , , and . Appl. Soft Comput., (February 2023)

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SmartCGMS as a Testbed for a Blood-Glucose Level Prediction and/or Control Challenge with (an FDA-Accepted) Diabetic Patient Simulation., and . EUSPN/ICTH, volume 177 of Procedia Computer Science, page 354-362. Elsevier, (2020)Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS.R for Diabetes Treatment., , , , , and . Sensors, 22 (23): 9445 (2022)Reducing high-risk glucose forecasting errors by evolving interpretable models for Type 1 diabetes., , , , , and . Appl. Soft Comput., (February 2023)An Evolution-based Machine Learning Approach for Inducing Glucose Prediction Models., , , , , and . ISCC, page 1-6. IEEE, (2022)Comparing the PaGMO Framework to a De-randomized Meta-Differential Evolution on Calculation and Prediction of Glucose Levels., , , , , , and . ISCC, page 1056-1061. IEEE, (2019)A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction., , , , , , and . Sensors, 23 (6): 2957 (March 2023)Grammatical Evolution-Based Approach for Extracting Interpretable Glucose-Dynamics Models., , , , , and . ISCC, page 1-6. IEEE, (2021)Parallel software architecture for the next generation of glucose monitoring., and . EUSPN/ICTH, volume 141 of Procedia Computer Science, page 279-286. Elsevier, (2018)SmartCGMS as an Environment for an Insulin-Pump Development with FDA-Accepted In-Silico Pre-Clinical Trials., and . EUSPN/ICTH, volume 160 of Procedia Computer Science, page 322-329. Elsevier, (2019)A Novel Approach to Multi-Compartmental Model Implementation to Achieve Metabolic Model Identifiability on Patient's CGM Data., and . EUSPN/ICTH, volume 210 of Procedia Computer Science, page 116-123. Elsevier, (2022)