Accuracy of devices for self-monitoring of blood glucose: A stochastic error model. - Martina Vettoretti; Andrea Facchinetti; Giovanni Sparacino; Claudio Cobelli (EMBC, 2015)

Patient decision-making of CGM sensor driven insulin therapies in type 1 diabetes: In silico assessment. - Martina Vettoretti; Andrea Facchinetti; Giovanni Sparacino; Claudio Cobelli (EMBC, 2015)

Online Calibration of Glucose Sensors From the Measured Current by a Time-Varying Calibration Function and Bayesian Priors. - Martina Vettoretti; Andrea Facchinetti; Simone Del Favero; Giovanni Sparacino; Claudio Cobelli (IEEE Trans. Biomed. Eng., 2016)

A Hybrid Clustering Prediction for Type 1 Diabetes Aid: Towards Decision Support Systems Based upon Scenario Profile Analysis. - Iván Contreras; Josep Vehí; Roberto Visentin; Martina Vettoretti (CHASE, 2017)

Bayesian Model Selection Framework to Improve Calibration of Continuous Glucose Monitoring Sensors for Diabetes Management. - Giada Acciaroli; Martina Vettoretti; Andrea Facchinetti; Giovanni Sparacino (EMBC, 2018)

Optimal Insulin Bolus Dosing in Type 1 Diabetes Management: Neural Network Approach Exploiting CGM Sensor Information. - Giacomo Cappon; Martina Vettoretti; Francesca Marturano; Andrea Facchinetti; Giovanni Sparacino (EMBC, 2018)

A Model of Acetaminophen Pharmacokinetics and its Effect on Continuous Glucose Monitoring Sensor Measurements. - Michele Schiavon; Giada Acciaroli; Martina Vettoretti; Alberto Giaretta 002; Roberto Visentin (EMBC, 2018)

Importance of Recalibrating Models for Type 2 Diabetes Onset Prediction: Application of the Diabetes Population Risk Tool on the Health and Retirement Study. - Martina Vettoretti; Enrico Longato; Barbara Di Camillo; Andrea Facchinetti (EMBC, 2018)

Reduction of Blood Glucose Measurements to Calibrate Subcutaneous Glucose Sensors: A Bayesian Multiday Framework. - Giada Acciaroli; Martina Vettoretti; Andrea Facchinetti; Giovanni Sparacino; Claudio Cobelli (IEEE Trans. Biomed. Eng., 2018)

Type-1 Diabetes Patient Decision Simulator for In Silico Testing Safety and Effectiveness of Insulin Treatments. - Martina Vettoretti; Andrea Facchinetti; Giovanni Sparacino; Claudio Cobelli (IEEE Trans. Biomed. Eng., 2018)

In-silico Assessment of Preventive Hypotreatment Efficacy and Development of a Continuous Glucose Monitoring Based Algorithm to Prevent/Mitigate Hypoglycemia in Type 1 Diabetes. - Nunzio Camerlingo; Martina Vettoretti; Simone Del Favero; Giacomo Cappon; Giovanni Sparacino; Andrea Facchinetti (EMBC, 2019)

Modeling the error of factory-calibrated continuous glucose monitoring sensors: application to Dexcom G6 sensor data. - Martina Vettoretti; Simone Del Favero; Giovanni Sparacino; Andrea Facchinetti (EMBC, 2019)

Development of an Error Model for a Factory-Calibrated Continuous Glucose Monitoring Sensor with 10-Day Lifetime. - Martina Vettoretti; Cristina Battocchio; Giovanni Sparacino; Andrea Facchinetti (Sensors, 2019)

A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models. - Enrico Longato; Martina Vettoretti; Barbara Di Camillo (J. Biomed. Informatics, 2020)

Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors. - Martina Vettoretti; Giacomo Cappon; Andrea Facchinetti; Giovanni Sparacino (Sensors, 2020)

A Dynamic Bayesian Network model for simulating the progression to diabetes onset in the ageing population. - Chiara Roversi; Erica Tavazzi; Martina Vettoretti; Barbara Di Camillo (BHI, 2021)

Predicting hypertension onset using logistic regression models with labs and/or easily accessible variables: the role of blood pressure measurements. - Chiara Roversi; Martina Vettoretti; Barbara Di Camillo; Andrea Facchinetti (BHI, 2021)

A Mathematical Formula to Determine the Minimum Continuous Glucose Monitoring Duration to Assess Time-in-ranges: Sensitivity Analysis Over the Parameters. - Nunzio Camerlingo; Martina Vettoretti; Giovanni Sparacino; Andrea Facchinetti; Julia K. Mader; Pratik Choudhary; Simone Del Favero (EMBC, 2021)

Data Gap Modeling in Continuous Glucose Monitoring Sensor Data. - Martina Drecogna; Martina Vettoretti; Simone Del Favero; Andrea Facchinetti; Giovanni Sparacino (EMBC, 2021)

Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only. - Francesco Prendin; Simone Del Favero; Martina Vettoretti; Giovanni Sparacino; Andrea Facchinetti (Sensors, 2021)

Machine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy. - Giulia Noaro; Giacomo Cappon; Martina Vettoretti; Giovanni Sparacino; Simone Del Favero; Andrea Facchinetti (IEEE Trans. Biomed. Eng., 2021)

Overview of iDPP@CLEF 2022: The Intelligent Disease Progression Prediction Challenge. - Alessandro Guazzo; Isotta Trescato; Enrico Longato; Enidia Hazizaj; Dennis Dosso; Guglielmo Faggioli; Giorgio Maria Di Nunzio; Gianmaria Silvello; Martina Vettoretti; Erica Tavazzi; Chiara Roversi; Piero Fariselli; et al. (CLEF (Working Notes), 2022)

Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2022. - Alessandro Guazzo; Isotta Trescato; Enrico Longato; Enidia Hazizaj; Dennis Dosso; Guglielmo Faggioli; Giorgio Maria Di Nunzio; Gianmaria Silvello; Martina Vettoretti; Erica Tavazzi; Chiara Roversi; Piero Fariselli; et al. (CLEF, 2022)

Baseline Machine Learning Approaches To Predict Amyotrophic Lateral Sclerosis Disease Progression. - Isotta Trescato; Alessandro Guazzo; Enrico Longato; Enidia Hazizaj; Chiara Roversi; Erica Tavazzi; Martina Vettoretti; Barbara Di Camillo (CLEF (Working Notes), 2022)

Generation of post-meal insulin correction boluses in type 1 diabetes simulation models for in-silico clinical trials: More realistic scenarios obtained using a decision tree approach. - Nunzio Camerlingo; Martina Vettoretti; Simone Del Favero; Andrea Facchinetti; Pratik Choudhary; Giovanni Sparacino (Comput. Methods Programs Biomed., 2022)

Overview of iDPP@CLEF 2023: The Intelligent Disease Progression Prediction Challenge. - Guglielmo Faggioli; Alessandro Guazzo; Stefano Marchesin 001; Laura Menotti; Isotta Trescato; Helena Aidos; Roberto Bergamaschi; Giovanni Birolo; Paola Cavalla; Adriano Chiò; Arianna Dagliati; Mamede de Carvalho; et al. (CLEF (Working Notes), 2023)

Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2023. - Guglielmo Faggioli; Alessandro Guazzo; Stefano Marchesin 001; Laura Menotti; Isotta Trescato; Helena Aidos; Roberto Bergamaschi; Giovanni Birolo; Paola Cavalla; Adriano Chiò; Arianna Dagliati; Mamede de Carvalho; et al. (CLEF, 2023)

Baseline Machine Learning Approaches To Predict Multiple Sclerosis Disease Progression. - Alessandro Guazzo; Isotta Trescato; Enrico Longato; Erica Tavazzi; Martina Vettoretti; Barbara Di Camillo (CLEF (Working Notes), 2023)

Investigating the Dynamics of Cardio-Metabolic Comorbidities and Their Interactions in Ageing Adults Through Dynamic Bayesian Networks. - Erica Tavazzi; Chiara Roversi; Martina Vettoretti; Barbara Di Camillo (PKDD/ECML Workshops (4), 2023)

Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review. - Erica Tavazzi; Enrico Longato; Martina Vettoretti; Helena Aidos; Isotta Trescato; Chiara Roversi; Andreia S. Martins; Eduardo N. Castanho; Ruben Branco; Diogo F. Soares; Alessandro Guazzo; Giovanni Birolo; et al. (Artif. Intell. Medicine, 2023)

A Markov Model of Gap Occurrence in Continuous Glucose Monitoring Data for Realistic in Silico Clinical Trials. - Martina Vettoretti; Martina Drecogna; Simone Del Favero; Andrea Facchinetti; Giovanni Sparacino (Comput. Methods Programs Biomed., 2023)

iDPP@CLEF 2023 - Participants' repositories for the Intelligent Disease Prediction Progression Challenge. - Guglielmo Faggioli; Alessandro Guazzo; Stefano Marchesin 001; Laura Menotti; Isotta Trescato; Helena Aidos; Roberto Bergamaschi; Giovanni Birolo; Paola Cavalla; Adriano Chiò; Arianna Dagliati; Mamede de Carvalho; et al. (Zenodo, 2023)

ReplayBG: A Digital Twin-Based Methodology to Identify a Personalized Model From Type 1 Diabetes Data and Simulate Glucose Concentrations to Assess Alternative Therapies. - Giacomo Cappon; Martina Vettoretti; Giovanni Sparacino; Simone Del Favero; Andrea Facchinetti (IEEE Trans. Biomed. Eng., 2023)

A model to forecast the two-year variation of subjective wellbeing in the elderly population. - Isotta Trescato; Chiara Roversi; Martina Vettoretti; Barbara Di Camillo; Andrea Facchinetti (BMC Medical Informatics Decis. Mak., 2023)

Machine Learning Models Highlight the Impact of Pollution and Weather Patterns on Relapse Occurrence in Multiple Sclerosis Patients. - Elena Marinello; Erica Tavazzi; Enrico Longato; Pietro Bosoni; Arianna Dagliati; Mahin Vazifehdan; Riccardo Bellazzi; Isotta Trescato; Alessandro Guazzo; Martina Vettoretti; Eleonora Tavazzi; Lara Ahmad; et al. (BIBM, 2024)

Overview of iDPP@CLEF 2024: The Intelligent Disease Progression Prediction Challenge. - Giovanni Birolo; Pietro Bosoni; Guglielmo Faggioli; Helena Aidos; Roberto Bergamaschi; Paola Cavalla; Adriano Chiò; Arianna Dagliati; Mamede de Carvalho; Giorgio Maria Di Nunzio; Piero Fariselli; Jose Manuel García Dominguez; et al. (CLEF (Working Notes), 2024)

Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2024. - Giovanni Birolo; Pietro Bosoni; Guglielmo Faggioli; Helena Aidos; Roberto Bergamaschi; Paola Cavalla; Adriano Chiò; Arianna Dagliati; Mamede de Carvalho; Giorgio Maria Di Nunzio; Piero Fariselli; Jose Manuel García Dominguez; et al. (CLEF (2), 2024)

Using Wearable and Environmental Data to Improve the Prediction of Amyotrophic Lateral Sclerosis and Multiple Sclerosis Progression: an Explorative Study. - Elena Marinello; Alessandro Guazzo; Enrico Longato; Erica Tavazzi; Isotta Trescato; Martina Vettoretti; Barbara Di Camillo (CLEF (Working Notes), 2024)

Optimizing Remote Health Monitoring for Digital Platforms: Evaluating the Efficacy of Gamification in Enhancing Data Collection. - Luca Cossu; Martina Vettoretti; Giacomo Cappon (MIE, 2024)

Unveiling Trustworthy AI Challenges: Characterizing Prediction Reliability. - Isotta Trescato; Alessandro Guazzo; Enrico Longato; Erica Tavazzi; Martina Vettoretti; Umberto Manera; Adriano Chiò; Marta Gromicho; Inês Alves; Mamede de Carvalho; Barbara Di Camillo (RTSI, 2024)

Corrigendum to "Generation of post-meal insulin correction boluses in type 1 diabetes simulation models for in-silico clinical trials: More realistic scenarios obtained using a decision tree approach". - Nunzio Camerlingo; Martina Vettoretti; Simone Del Favero; Andrea Facchinetti; Pratik Choudhary; Giovanni Sparacino (Comput. Methods Programs Biomed., 2024)

Risk of hypoglycemia in type 1 diabetes management: An in-silico sensitivity analysis to assess and rank the quantitative impact of different behavioral factors. - Chiara Roversi; Nunzio Camerlingo; Martina Vettoretti; Andrea Facchinetti; Pratik Choudhary; Giovanni Sparacino; Simone Del Favero (Comput. Methods Programs Biomed., 2024)

Exploring the Impact of Environmental Pollutants on Multiple Sclerosis Progression. - Elena Marinello; Erica Tavazzi; Enrico Longato; Pietro Bosoni; Arianna Dagliati; Mahin Vazifehdan; Riccardo Bellazzi; Isotta Trescato; Alessandro Guazzo; Martina Vettoretti; Eleonora Tavazzi; Lara Ahmad; et al. (CoRR, 2024)

BRAINTEASER ALS and MS Datasets. - Guglielmo Faggioli; Stefano Marchesin 001; Laura Menotti; Helena Aidos; Roberto Bergamaschi; Giovanni Birolo; Pietro Bosoni; Paola Cavalla; Adriano Chiò; Arianna Dagliati; Mamede de Carvalho; Giorgio Maria Di Nunzio; et al. (Zenodo, 2024)

iDPP@CLEF 2024 - Participants' repositories for the Intelligent Disease Prediction Progression Challenge. - Giovanni Birolo; Pietro Bosoni; Guglielmo Faggioli; Helena Aidos; Roberto Bergamaschi; Paola Cavalla; Adriano Chiò; Arianna Dagliati; Mamede de Carvalho; Giorgio Maria Di Nunzio; Piero Fariselli; Jose Manuel García Dominguez; et al. (Zenodo, 2024)

Predicting clinical events characterizing the progression of amyotrophic lateral sclerosis via machine learning approaches using routine visits data: a feasibility study. - Alessandro Guazzo; Michele Atzeni; Elena Idi; Isotta Trescato; Erica Tavazzi; Enrico Longato; Umberto Manera; Adriano Chiò; Marta Gromicho; Inês Alves; Mamede de Carvalho; Martina Vettoretti; et al. (BMC Medical Informatics Decis. Mak., 2024)

DYNAMITE: Integrating Archetypal Analysis and Process Mining for Interpretable Disease Progression Modelling. - Isotta Trescato; Erica Tavazzi; Martina Vettoretti; Roberto Gatta; Rosario Vasta; Adriano Chiò; Barbara Di Camillo (IEEE J. Biomed. Health Informatics, 2024)

BRAINTEASER ALS and MS Datasets. - Guglielmo Faggioli; Stefano Marchesin 001; Laura Menotti; Helena Aidos; Roberto Bergamaschi; Giovanni Birolo; Pietro Bosoni; Paola Cavalla; Adriano Chiò; Arianna Dagliati; Mamede de Carvalho; Giorgio Maria Di Nunzio; et al. (Zenodo, 2025)