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Giovanni Sparacino

Giovanni Sparacino

D-Index & Metrics

Engineering and Technology

D-Index
46
Citations
7569
World Ranking
5196
National Ranking
157

Overview

Giovanni Sparacino is affiliated with the University of Padua in Italy, focusing extensively on medicine with a specialization in endocrinology, diabetes, and metabolism. Their academic contributions span over 119 publications in medicine, with 76 specifically addressing endocrinology, diabetes, and metabolism. Additionally, their research encompasses surgery, genetics, cognitive neuroscience, and artificial intelligence, reflecting a multidisciplinary approach to health and technology.

The primary topics of Giovanni Sparacino's research include diabetes management and research, diabetes treatment and management, pancreatic function and diabetes, diabetes and associated disorders, machine learning in healthcare, EEG and brain-computer interfaces, and the intersection of metabolism, diabetes, and cancer.

Frequent publication venues for Sparacino's work include:

  • Journal of Diabetes Science and Technology
  • Computer Methods and Programs in Biomedicine
  • Scientific Reports
  • Cardiovascular Diabetology
  • Sensors

Among the recent papers authored by or associated with Giovanni Sparacino are:

  • Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors, 2020, Sensors
  • The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP, 2023, Scientific Reports
  • Exposure to dipeptidyl-peptidase-4 inhibitors and COVID-19 among people with type 2 diabetes: A case-control study, 2020, Diabetes Obesity and Metabolism
  • Cardiovascular outcomes of type 2 diabetic patients treated with SGLT-2 inhibitors versus GLP-1 receptor agonists in real-life, 2020, BMJ Open Diabetes Research & Care
  • A Deep Learning Approach to Predict Diabetes' Cardiovascular Complications From Administrative Claims, 2021, IEEE Journal of Biomedical and Health Informatics

Giovanni Sparacino has collaborated regularly with several co-authors, including:

  • Andrea Facchinetti
  • Simone Del Favero
  • Enrico Longato
  • Gian Paolo Fadini
  • Barbara Di Camillo

This researcher's work integrates advanced computational methods such as artificial intelligence and machine learning, especially applied to glucose monitoring and diabetes care, reflecting significant interdisciplinary engagement. Their publication record demonstrates a strong emphasis on using data-driven approaches to address clinical questions related to diabetes management and cardiovascular outcomes.

Best Publications

  • Diabetes: Models, Signals, and Control

    C. Cobelli;C. Dalla Man;G. Sparacino;L. Magni

  • Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications

    Giacomo Cappon;Martina Vettoretti;Giovanni Sparacino;Andrea Facchinetti

  • Glucose Concentration can be Predicted Ahead in Time From Continuous Glucose Monitoring Sensor Time-Series

    G. Sparacino;F. Zanderigo;S. Corazza;A. Maran

  • Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.

    C Pérez-Gandía;A Facchinetti;G Sparacino;C Cobelli

  • Nonparametric input estimation in physiological systems: problems, methods, and case studies

    Giuseppe de Nicolao;Giovanni Sparacino;Claudio Cobelli

  • Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

    Giacomo Cappon;Giada Acciaroli;Martina Vettoretti;Andrea Facchinetti

  • Neural Network Incorporating Meal Information Improves Accuracy of Short-Time Prediction of Glucose Concentration

    C. Zecchin;A. Facchinetti;G. Sparacino;G. De Nicolao

  • Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors.

    Martina Vettoretti;Giacomo Cappon;Andrea Facchinetti;Giovanni Sparacino

  • Modeling the Glucose Sensor Error

    Andrea Facchinetti;Simone Del Favero;Giovanni Sparacino;Jessica R. Castle

  • Real-Time Improvement of Continuous Glucose Monitoring Accuracy: The smart sensor concept

    Andrea Facchinetti;Giovanni Sparacino;Stefania Guerra;Yoeri M. Luijf

  • Calibration of Minimally Invasive Continuous Glucose Monitoring Sensors: State-of-The-Art and Current Perspectives.

    Giada Acciaroli;Martina Vettoretti;Andrea Facchinetti;Giovanni Sparacino

  • "Smart" Continuous Glucose Monitoring Sensors: On-Line Signal Processing Issues

    Giovanni Sparacino;Andrea Facchinetti;Claudio Cobelli

  • Numerical non-identifiability regions of the minimal model of glucose kinetics: superiority of Bayesian estimation.

    Gianluigi Pillonetto;Giovanni Sparacino;Claudio Cobelli

  • Type-1 Diabetes Patient Decision Simulator for In Silico Testing Safety and Effectiveness of Insulin Treatments

    Martina Vettoretti;Andrea Facchinetti;Giovanni Sparacino;Claudio Cobelli

  • Enhancing the Accuracy of Subcutaneous Glucose Sensors: A Real-Time Deconvolution-Based Approach

    S. Guerra;A. Facchinetti;G. Sparacino;G. De Nicolao

  • Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information

    C. Zecchin;A. Facchinetti;G. Sparacino;C. Cobelli

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

    Andrea Facchinetti;Simone Del Favero;Giovanni Sparacino;Claudio Cobelli

  • Exposure to dipeptidyl-peptidase-4 inhibitors and COVID-19 among people with type 2 diabetes: A case-control study.

    Gian Paolo Fadini;Mario Luca Morieri;Enrico Longato;Benedetta Maria Bonora

  • Continuous glucose monitoring time series and hypo/hyperglycemia prevention: requirements, methods, open problems.

    Giovanni Sparacino;Andrea Facchinetti;Alberto Maran;Claudio Cobelli

  • Continuous Glucose Monitoring: Current Use in Diabetes Management and Possible Future Applications:

    Martina Vettoretti;Giacomo Cappon;Giada Acciaroli;Andrea Facchinetti

  • A new neural network approach for short-term glucose prediction using continuous glucose monitoring time-series and meal information

    C. Zecchin;A. Facchinetti;G. Sparacino;G. De Nicolao

Frequent Co-Authors

Andrea Facchinetti
Andrea Facchinetti University of Padua
Claudio Cobelli
Claudio Cobelli University of Padua
Angelo Avogaro
Angelo Avogaro University of Padua
Gian Paolo Fadini
Gian Paolo Fadini University of Padua
Gianluigi Pillonetto
Gianluigi Pillonetto University of Padua
Roberto Dell'Acqua
Roberto Dell'Acqua University of Padua
Giuseppe De Nicolao
Giuseppe De Nicolao University of Pavia
Riccardo Bellazzi
Riccardo Bellazzi University of Pavia
Patrizia Bisiacchi
Patrizia Bisiacchi University of Padua
Tiinamaija Tuomi
Tiinamaija Tuomi University of Helsinki

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