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Safety and feasibility of the PEPPER adaptive bolus advisor and safety system: a randomized control study (2020)
Journal Article
Avari, P., Leal, Y., Herrero, P., Wos, M., Jugnee, N., Arnoriaga-Rodríguez, M., …Reddy, M. (2021). Safety and feasibility of the PEPPER adaptive bolus advisor and safety system: a randomized control study. Diabetes Technology and Therapeutics, 23(3), 175-186. https://doi.org/10.1089/dia.2020.0301

Background: The Patient Empowerment through Predictive Personalised Decision Support (PEPPER) system provides personalised bolus advice for people with Type 1 diabetes. The system incorporates an adaptive insulin recommender system (based on case-bas... Read More about Safety and feasibility of the PEPPER adaptive bolus advisor and safety system: a randomized control study.

Long-Term Glucose Forecasting Using a Physiological Model and Deconvolution of the Continuous Glucose Monitoring Signal (2019)
Journal Article
Liu, C., Vehí, J., Avari, P., Reddy, M., Oliver, N., Georgiou, P., & Herrero, P. (2019). Long-Term Glucose Forecasting Using a Physiological Model and Deconvolution of the Continuous Glucose Monitoring Signal. Sensors, 19(19), Article 4338. https://doi.org/10.3390/s19194338

(1) Objective: Blood glucose forecasting in type 1 diabetes (T1D) management is a maturing field with numerous algorithms being published and a few of them having reached the commercialisation stage. However, accurate long-term glucose predictions (e... Read More about Long-Term Glucose Forecasting Using a Physiological Model and Deconvolution of the Continuous Glucose Monitoring Signal.

GluNet: A Deep Learning Framework For Accurate Glucose Forecasting (2019)
Journal Article
Li, K., Liu, C., Zhu, T., Herrero, P., & Georgiou, P. (2019). GluNet: A Deep Learning Framework For Accurate Glucose Forecasting. IEEE Journal of Biomedical and Health Informatics, 24(2), 414-423. https://doi.org/10.1109/jbhi.2019.2931842

For people with Type 1 diabetes (T1D), forecasting of \red{blood glucose (BG)} can be used to effectively avoid hyperglycemia, hypoglycemia and associated complications. The latest continuous glucose monitoring (CGM) technology allows people to obser... Read More about GluNet: A Deep Learning Framework For Accurate Glucose Forecasting.

A Modular Safety System for an Insulin Dose Recommender: A Feasibility Study (2019)
Journal Article
Liu, C., Avari, P., Leal, Y., Wos, M., Sivasithamparam, K., Georgiou, P., …Herrero, P. (2020). A Modular Safety System for an Insulin Dose Recommender: A Feasibility Study. Journal of Diabetes Science and Technology, 14(1), 87-96. https://doi.org/10.1177/1932296819851135

© 2019 Diabetes Technology Society. Background: Delivering insulin in type 1 diabetes is a challenging, and potentially risky, activity; hence the importance of including safety measures as part of any insulin dosing or recommender system. This work... Read More about A Modular Safety System for an Insulin Dose Recommender: A Feasibility Study.

Full‐complexity polytopic robust control invariant sets for uncertain linear discrete‐time systems (2019)
Journal Article
Liu, C., Tahir, F., & Jaimoukha, I. M. (2019). Full‐complexity polytopic robust control invariant sets for uncertain linear discrete‐time systems. International Journal of Robust and Nonlinear Control, 29(11), 3587-3605. https://doi.org/10.1002/rnc.4573

This paper presents an algorithm for the computation of full‐complexity polytopic robust control invariant (RCI) sets, and the corresponding linear state‐feedback control law. The proposed scheme can be applied for linear discrete‐time systems subjec... Read More about Full‐complexity polytopic robust control invariant sets for uncertain linear discrete‐time systems.

Convolutional Recurrent Neural Networks for Glucose Prediction (2019)
Journal Article
Li, K., Daniels, J., Liu, C., Herrero-Vinas, P., & Georgiou, P. (2020). Convolutional Recurrent Neural Networks for Glucose Prediction. IEEE Journal of Biomedical and Health Informatics, 24(2), 603-613. https://doi.org/10.1109/jbhi.2019.2908488

© 2013 IEEE. Control of blood glucose is essential for diabetes management. Current digital therapeutic approaches for subjects with type 1 diabetes mellitus such as the artificial pancreas and insulin bolus calculators leverage machine learning tech... Read More about Convolutional Recurrent Neural Networks for Glucose Prediction.