Facchinetti, Andrea

Continuous glucose monitoring sensors for diabetes management: A review of technologies and applications

Cappon, G., Vettoretti, M., Sparacino, G., & Facchinetti, A. (2019) · Diabetes & Metabolism Journal

Diabetes Management and ResearchElectrochemical sensors and biosensors

Cited by 477

Multinational Study of Subcutaneous Model-Predictive Closed-Loop Control in Type 1 Diabetes Mellitus: Summary of the Results

Kovatchev, B., Cobelli, C., Renard, E., Anderson, S., Breton, M., Patek, S., Clarke, W., Bruttomesso, D., Maran, A., Costa, S., et al. (2010) · Journal of Diabetes Science and Technology

Diabetes Management and ResearchDiabetes Treatment and Management

Cited by 200

Continuous glucose monitoring sensors: past, present and future algorithmic challenges

Facchinetti, A. (2016) · Sensors

Diabetes Management and ResearchElectrochemical sensors and biosensors

Cited by 176

Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors

Vettoretti, M., Cappon, G., Facchinetti, A., & Sparacino, G. (2020) · Sensors

Diabetes Management and ResearchDiabetes Management and Education

Cited by 176

Modeling the glucose sensor error

Facchinetti, A., Del Favero, S., Sparacino, G., Castle, J. R., Ward, W. K., & Cobelli, C. (2014) · IEEE Transactions on Biomedical Engineering

Diabetes Management and ResearchControl Systems and Identification

Cited by 130

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities

Jacobs, P. G., Herrero, P., Facchinetti, A., Vehi, J., Kovatchev, B., Breton, M. D., Cinar, A., Nikita, K. S., Doyle, F. J., Bondia, J., et al. (2024) · IEEE Reviews in Biomedical Engineering

Diabetes Management and ResearchArtificial Intelligence in Healthcare

Cited by 113

Continuous glucose monitoring: Current use in diabetes management and possible future applications

Vettoretti, M., Cappon, G., Acciaroli, G., Facchinetti, A., & Sparacino, G. (2018) · Journal of Diabetes Science and Technology

Diabetes Management and ResearchDiabetes Management and Education

Cited by 96

Model of glucose sensor error components: identification and assessment for new Dexcom G4 generation devices

Facchinetti, A., Del Favero, S., Sparacino, G., & Cobelli, C. (2015) · Medical & Biological Engineering & Computing

Diabetes Management and ResearchElectrochemical sensors and biosensors

Cited by 80

How much is short-term glucose prediction in type 1 diabetes improved by adding insulin delivery and meal content information to CGM data? A Proof-of-Concept Study

Zecchin, C., Facchinetti, A., Sparacino, G., & Cobelli, C. (2016) · Journal of Diabetes Science and Technology

Diabetes Management and ResearchDiabetes Management and Education

Cited by 64

A Neural-network-based approach to personalize insulin bolus calculation using continuous glucose monitoring

Cappon, G., Vettoretti, M., Marturano, F., Facchinetti, A., & Sparacino, G. (2018) · Journal of Diabetes Science and Technology

Diabetes Management and ResearchDiabetes Management and Education

Cited by 62

An Online Failure Detection Method of the Glucose Sensor-Insulin Pump System: Improved Overnight Safety of Type-1 Diabetic Subjects

Facchinetti, A., Favero, S., Sparacino, G., & Cobelli, C. (2013) · IEEE Transactions on Biomedical Engineering

Diabetes Management and ResearchFault Detection and Control Systems

Cited by 62

Machine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy

Noaro, G., Cappon, G., Vettoretti, M., Sparacino, G., Favero, S. D., & Facchinetti, A. (2021) · IEEE Transactions on Biomedical Engineering

Diabetes Management and ResearchDiabetes Management and Education

Cited by 57

A real-time continuous glucose monitoring-based algorithm to trigger hypotreatments to prevent/mitigate hypoglycemic events

Camerlingo, N., Vettoretti, M., Del Favero, S., Cappon, G., Sparacino, G., & Facchinetti, A. (2019) · Diabetes Technology & Therapeutics

Diabetes Management and ResearchDiabetes Treatment and Management

Cited by 22

What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project

Fico, G., Hernanzez, L., Cancela, J., Dagliati, A., Sacchi, L., Martinez-Millana, A., Posada, J., Manero, L., Verdú, J., Facchinetti, A., et al. (2019) · BMC Medical Informatics and Decision Making

Electronic Health Records SystemsTechnology and Human Factors in Education and Health

Cited by 22

An Integrated Mobile Platform for Automated Data Collection and Real-Time Patient Monitoring in Diabetes Clinical Trials

Cappon, G., Cossu, L., Boscari, F., Bruttomesso, D., Sparacino, G., & Facchinetti, A. (2022) · Journal of Diabetes Science and Technology

Mobile Health and mHealth ApplicationsDiabetes Management and Research

Cited by 18

In Silico Assessment of literature insulin bolus calculation methods accounting for glucose rate of change

Cappon, G., Marturano, F., Vettoretti, M., Facchinetti, A., & Sparacino, G. (2019) · Journal of Diabetes Science and Technology

Diabetes Management and ResearchDiabetes Management and Education

Cited by 18

Digital Solutions to Diagnose and Manage Postbariatric Hypoglycemia

Schönenberger, K. A., Cossu, L., Prendin, F., Cappon, G., Wu, J., Fuchs, K. L., Mayer, S., Herzig, D., Facchinetti, A., & Bally, L. (2022) · Frontiers in Nutrition

Bariatric Surgery and OutcomesMobile Health and mHealth Applications

Cited by 16

Design and Usability Assessment of a User-Centered, Modular Platform for Real-World Data Acquisition in Clinical Trials Involving Post-Bariatric Surgery Patients

Cossu, L., Cappon, G., Streicher, O., Herzig, D., Bally, L., & Facchinetti, A. (2025) · Journal of Diabetes Science and Technology

Mobile Health and mHealth ApplicationsUsability and User Interface Design

Cited by 1