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Michele Bernardini

Showing results (1-10 of 9) with videos related to

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IEEE Journal of Biomedical and Health Informatics|February 15, 2019
Discovering the Type 2 Diabetes in Electronic Health Records Using the Sparse Balanced Support Vector MachineMichele Bernardini, Luca Romeo, Paolo Misericordia, et al.
IEEE Journal of Biomedical and Health Informatics|April 20, 2021
A Semi-Supervised Multi-Task Learning Approach for Predicting Short-Term Kidney Disease EvolutionMichele Bernardini, Luca Romeo, Emanuele Frontoni, et al.
Computers in Biology and Medicine|July 24, 2019
TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health RecordsMichele Bernardini, Micaela Morettini, Luca Romeo, et al.
Artificial Intelligence in Medicine|June 8, 2020
Early temporal prediction of Type 2 Diabetes Risk Condition from a General Practitioner Electronic Health Record: A Multiple Instance Boosting ApproachMichele Bernardini, Micaela Morettini, Luca Romeo, et al.
Computers in Biology and Medicine|July 2, 2023
A novel missing data imputation approach based on clinical conditional Generative Adversarial Networks applied to EHR datasetsMichele Bernardini, Anastasiia Doinychko, Luca Romeo, et al.
IEEE Journal of Translational Engineering in Health and Medicine|November 5, 2020
A Decision Support System for Diabetes Chronic Care Models Based on General Practitioner Engagement and EHR Data SharingEmanuele Frontoni, Luca Romeo, Michele Bernardini, et al.
Diabetes Research and Clinical Practice|July 23, 2022
Prediction of complications of type 2 Diabetes: A Machine learning approachAntonio Nicolucci, Luca Romeo, Michele Bernardini, et al.
Diabetes Research and Clinical Practice|September 15, 2025
A machine learning algorithm for the prediction of complications incorporated in electronic medical records improves type 2 diabetes careAntonio Nicolucci, Giacomo Vespasiani, Domenico Mannino, et al.
Journal of Intensive Medicine|February 14, 2023
Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patientsJonathan Montomoli, Luca Romeo, Sara Moccia, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
IEEE Journal of Biomedical and Health Informatics|February 15, 2019
Discovering the Type 2 Diabetes in Electronic Health Records Using the Sparse Balanced Support Vector MachineMichele Bernardini, Luca Romeo, Paolo Misericordia, et al.
IEEE Journal of Biomedical and Health Informatics|April 20, 2021
A Semi-Supervised Multi-Task Learning Approach for Predicting Short-Term Kidney Disease EvolutionMichele Bernardini, Luca Romeo, Emanuele Frontoni, et al.
Computers in Biology and Medicine|July 24, 2019
TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health RecordsMichele Bernardini, Micaela Morettini, Luca Romeo, et al.
Artificial Intelligence in Medicine|June 8, 2020
Early temporal prediction of Type 2 Diabetes Risk Condition from a General Practitioner Electronic Health Record: A Multiple Instance Boosting ApproachMichele Bernardini, Micaela Morettini, Luca Romeo, et al.
Computers in Biology and Medicine|July 2, 2023
A novel missing data imputation approach based on clinical conditional Generative Adversarial Networks applied to EHR datasetsMichele Bernardini, Anastasiia Doinychko, Luca Romeo, et al.
IEEE Journal of Translational Engineering in Health and Medicine|November 5, 2020
A Decision Support System for Diabetes Chronic Care Models Based on General Practitioner Engagement and EHR Data SharingEmanuele Frontoni, Luca Romeo, Michele Bernardini, et al.
Diabetes Research and Clinical Practice|July 23, 2022
Prediction of complications of type 2 Diabetes: A Machine learning approachAntonio Nicolucci, Luca Romeo, Michele Bernardini, et al.
Diabetes Research and Clinical Practice|September 15, 2025
A machine learning algorithm for the prediction of complications incorporated in electronic medical records improves type 2 diabetes careAntonio Nicolucci, Giacomo Vespasiani, Domenico Mannino, et al.
Journal of Intensive Medicine|February 14, 2023
Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patientsJonathan Montomoli, Luca Romeo, Sara Moccia, et al.
Pageof 1