| Data-driven anomaly recognition for unsupervised model-free fault detection in artificial pancreas L Meneghetti, M Terzi, S Del Favero, GA Susto, C Cobelli IEEE Transactions on Control Systems Technology 28 (1), 33-47, 2018 | 76 | 2018 |
| Model-based detection and classification of insulin pump faults and missed meal announcements in artificial pancreas systems for type 1 diabetes therapy L Meneghetti, A Facchinetti, S Del Favero IEEE Transactions on Biomedical Engineering 68 (1), 170-180, 2020 | 32 | 2020 |
| Detection of insulin pump malfunctioning to improve safety in artificial pancreas using unsupervised algorithms L Meneghetti, GA Susto, S Del Favero Journal of diabetes science and technology 13 (6), 1065-1076, 2019 | 25 | 2019 |
| A Personalized and Interpretable Deep Learning Based Approach to Predict Blood Glucose Concentration in Type 1 Diabetes. G Cappon, L Meneghetti, F Prendin, J Pavan, G Sparacino, S Del Favero, ... KDH@ ECAI 20, 75-79, 2020 | 20 | 2020 |
| Personalized Machine Learning Algorithm based on Shallow Network and Error Imputation Module for an Improved Blood Glucose Prediction. J Pavan, F Prendin, L Meneghetti, G Cappon, G Sparacino, A Facchinetti, ... KDH@ ECAI, 95-99, 2020 | 12 | 2020 |
| Machine learning-based anomaly detection algorithms to alert patients using sensor augmented pump of infusion site failures L Meneghetti, E Dassau, FJ Doyle III, S Del Favero Journal of Diabetes Science and Technology 16 (3), 641-648, 2022 | 10 | 2022 |
| Fault detection in artificial pancreas: A model-free approach L Meneghetti, M Terzi, GA Susto, S Del Favero, C Cobelli 2018 IEEE Conference on Decision and Control (CDC), 303-308, 2018 | 6 | 2018 |
| A MACHINE LEARNING APPROACH FOR DETECTING INSULIN PUMP FAULTS L Meneghetti DIABETES TECHNOLOGY & THERAPEUTICS 22, A112-A113, 2020 | | 2020 |
| A MODEL-FREE APPROACH TO INSULIN PUMP FAILURES DETECTION L Meneghetti, M Terzi, GA Susto, S Del Favero, C Cobelli DIABETES TECHNOLOGY & THERAPEUTICS 21, A84-A84, 2019 | | 2019 |
| System for detecting malfunctions in insulin delivery devices S Del Favero, L Meneghetti, M Terzi, GA Susto, C Cobelli | | 2019 |
| Real-time basal insulin attenuation based on continuous glucose monitoring (CGM): assessment of state-of-art algorithms in 14-day in silico scenario M Vettoretti, C Fanton, A Facchinetti, L Meneghetti, G Sparacino, ... DIABETES TECHNOLOGY & THERAPEUTICS 19, 111-112, 2017 | | 2017 |
| In Silico Assessment of State-of-Art and New Algorithms for Real-time Basal Insulin Modulation in Sensor-Augmented Pumps M Vettoretti, L Meneghetti, F Chiara, G Sparacino, A Facchinetti Conference Proceedings of the 17th Annual Diabetes Technology Meeting (DTM), 2017 | | 2017 |
| Regolamentazione ambientale e offshoring: un'analisi empirica su un campione di imprese manifatturiere italiane L Meneghetti | | |
| Preparazione e caratterizzazione di nanoparticelle di ossido di ferro per ipertermia magnetica da coprecipitazione di sali di ferro L Meneghetti | | |
| Continuous glucose monitoring based algorithms for basal insulin therapy modulation in type 1 diabetes L Meneghetti | | |
Simone Del FaveroAssociate Professor, Department of Information Engineering, University of PadovaVerified email at dei.unipd.it
