Supporting research

Research on ReplayBG development

Journal Papers

• F. Prendin, A. Facchinetti, and G. Cappon “Data Augmentation via Digital Twins Enables the Development of Personalized Deep Learning Glucose Prediction Algorithms for Type 1 Diabetes in Poor Data Context” IEEE Journal of Biomedical Health Informatics, (under revision, submitted October 2024)
• G. Cappon, and A. Facchinetti, “Digital twins in type 1 diabetes: A systematic review” Journal of Diabetes Science and Technology, online ahead of print, 2024, DOI: 10.1177/1932296824126211.
• G. Cappon, M. Vettoretti, G. Sparacino, S. Del Favero, and A. Facchinetti “ReplayBG: a digital twin-based methodology to identify a personalized model from type 1 diabetes data and simulate glucose concentrations to assess alternative therapies”, IEEE Transactions on Biomedical Engineering, vol. 70, no. 11, pp. 3105-3115, Nov 2023, DOI: 10.1109/TBME.2023.3286856

Conference papers and abstracts

• G. Cappon, S. Del Favero, and A. Facchinetti “Extending the applicability of ReplayBG tool for digital twinning in type 1 diabetes from single-meal to multi-day scenarios” in the 24th Annual Diabetes Technology Meeting – DTM, Burlingame, CA, USA, October 15-17, 2024.
• J. Leth, H. Ghazaleh, H. Peuscher, and G. Cappon “The potential of open-source software in diabetes research” in the 17th International Conference on Advanced Technology & Treatment for Diabetes – ATTD, Firenze, Italy, March 6-9, 2024 (invited oral presentation, speaker J. Leth).
• H. Peuscher, G. Cappon, A. Cinar, J. Deichmann, H. Ghazaleh, H. Kaltenbach, L. Sandini, M. Siket, J. Xie, and X. Zhou “A survey on existing open-source projects in diabetes simulation” in the 17th International Conference on Advanced Technology & Treatment for Diabetes – ATTD, Firenze, Italy, March 6-9, 2024 (invited oral presentation, speaker J. Leth).
• G. Cappon, M. Vettoretti, G. Sparacino, S. Del Favero, and A. Facchinetti “Expanding ReplayBG simulation methodology domain of validity to single day multiple meal scenarios” in the 15th International Conference on Advanced Technology & Treatment for Diabetes – ATTD, Barcelona, Spain, April 27-30, 2022.
• G. Cappon, M. Vettoretti, G. Sparacino, S. Del Favero, and A. Facchinetti “ReplayBG provides reliable indications when used to assess meal bolus alterations in type 1 diabetes” in the 14th International Conference on Advanced Technology & Treatment for Diabetes – ATTD, Paris, France, June 2-6, 2021. G. Cappon, A. Facchinetti, G. Sparacino, and S. Del Favero “ReplayBG: A novel in-silico framework to retrospectively assess new therapy guidelines for type 1 diabetes management” in the 20th Annual Diabetes Technology Meeting – DTM, Bethesda, Maryland, USA, November 12-14, 2020.
• G. Cappon, A. Facchinetti, G. Sparacino, and S. Del Favero “A Bayesian framework to personalize glucose prediction in type 1 diabetes using a physiological model” in the 19th Annual Diabetes Technology Meeting – DTM, Bethesda, Maryland, USA, November 14-16, 2019.
• G. Cappon, A. Facchinetti, G. Sparacino, and S. Del Favero “A Bayesian framework to identify type 1 diabetes physiological models using easily accessible patient data” in the 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society – EMBC, Berlin, Germany, July 23-27, 2019 (accepted for oral presentation, speaker G.Cappon).

Research using ReplayBG as component or validation tool

Journal Papers

• E. Pellizzari, G. Cappon, G. Nicolis, G. Sparacino, and A. Facchinetti “Developing effective machine learning models for insulin bolus calculation in type 1 diabetes exploiting real-world data and digital twins” IEEE Transactions on Biomedical Engineering, (under revision, submitted October 2024)
• E. Pellizzari, F. Prendin, G. Cappon, G. Sparacino, and A. Facchinetti, “drCORRECT: An algorithm for the preventive administration of postprandial corrective insulin boluses in type 1 diabetes management” Journal of Diabetes Science and Technology, online ahead of print, 2023, DOI: 10.1177/19322968231221768
• F. Prendin, J. Pavan, G. Cappon, S. Del Favero, G. Sparacino, and A. Facchinetti, “The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP” Scientific Reports, vol. 13, no. 1, pp. 16865, Oct 2023, DOI: 10.1038/s41598-023-44155-x.
• G. Noaro, G. Cappon, M. Vettoretti, G. Sparacino, S. Del Favero, and A. Facchinetti “Machine-learning based model to improve insulin bolus calculation in type 1 diabetes therapy” IEEE Transactions on Biomedical Engineering, vol. 68, no. 1, pp. 247-255, Jan 2021. DOI: 10.1109/TBME.2020.3004031

Conference papers and abstracts

• E. Pellizzari, G. Cappon, G. Nicolis, G. Sparacino, and A. Facchinetti “Exploiting real-world data and digital twins to develop effective formulas for dosing insulin boluses in type 1 diabetes therapy” in the 24th Annual Diabetes Technology Meeting – DTM, Burlingame, CA, USA, October 15-17, 2024.
• F. Prendin, A. Facchinetti, and G. Cappon “Digital twin for data augmentation enables the development of accurate personalized deep glucose forecasting algorithms” in the 24th Annual Diabetes Technology Meeting – DTM, Burlingame, CA, USA, October 15-17, 2024.
• E. Pellizzari, F. Prendin, G. Cappon, G. Sparacino, and A. Facchinetti “DR-CIB: An algorithm for the preventive administration of corrective insulin boluses in T1D based on dynamic risk concept and patient-specific timing” in the 23rd Annual Diabetes Technology Meeting – DTM, Online, November 1-4, 2023 (Gold Student Award, accepted for oral presentation, speaker E. Pellizzari).
• G. Cappon, E. Pellizzari, L. Cossu, G. Sparacino, A. Deodati, R. Schiaffini, S. Cianfarani, and A. Facchinetti “System architecture of TWIN: A new digital twin-based clinical decision support system for type 1 diabetes management in children” in the 19th International Conference on Body Sensor Networks – BSN, Boston, MA, USA, October 9-11, 2023.
• E. Pellizzari, F. Prendin, G. Cappon, G. Sparacino, and A. Facchinetti “A deep-learning based algorithm for the management of hyperglycemia in type 1 diabetes therapy” in the 19th International Conference on Body Sensor Networks – BSN, Boston, MA, USA, October 9-11, 2023.
• G. Noaro, G. Cappon, G. Sparacino, and A. Facchinetti “An ensemble learning algorithm based on dynamic voting for targeting the optimal insulin dosage in type 1 diabetes management“ in the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society – EMBC, Guadalajara, Mexico, October 31-4, 2021 (accepted for oral presentation, speaker G. Noaro).
• G. Cappon, E. Pighin, F. Prendin, G. Sparacino, and A. Facchinetti “A correction insulin bolus delivery strategy for decision support systems in type 1 diabetes“ in the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society – EMBC, Guadalajara, Mexico, October 31-4, 2021 (accepted for oral presentation, speaker G. Cappon).
• G. Noaro, G. Cappon, M. Vettoretti, S. Del Favero, G. Sparacino, and A. Facchinetti “A new model for mealtime insulin dosing in type 1 diabetes: Retrospective validation on CTR3 dataset” in the 20th Annual Diabetes Technology Meeting – DTM, Bethesda, Maryland, USA, November 12-14, 2020 (accepted for oral presentation, speaker G. Noaro).
• G. Noaro, G. Cappon, G. Sparacino, S. Del Favero, A. Facchinetti “Nonlinear machine learning models for insulin bolus estimation in type 1 diabetes therapy” in the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society – EMBC, Montreal, Canada, July 20-24, 2020 (accepted for oral presentation, speaker G. Noaro)