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Comparing the PaGMO Framework to a De-randomized Meta-Differential Evolution on Calculation and Prediction of Glucose Levels.

, , , , , , и . ISCC, стр. 1056-1061. IEEE, (2019)

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