We propose a layered meta-learning approach based on multi-expert systems to predict adverse events in T1D. The base learner is composed of three deep neural networks and exploits only continuous glucose monitoring data as an input feature.
Jan 18, 2023
Jan 15, 2023 · We propose a layered meta-learning approach based on multi-expert systems to predict adverse events in T1D. The base learner is composed of ...
Dec 20, 2016 · The main objective of T1D control is to correct hyperglycemia while avoiding hypoglycemia [13]. Although CGM sensors are widely adopted by ...
Feb 1, 2023 · Layered Meta-Learning Algorithm for Predicting Adverse Events in Type 1 Diabetes We propose a layered meta-learning approach based on multi ...
Layered meta-learning algorithm for predicting adverse events in type 1 diabetes. F D'Antoni, L Petrosino, A Marchetti, L Bacco, S Pieralice, L Vollero, ...
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Layered meta-learning Algorithm for Predicting Adverse Events in Type 1 Diabetes · D'antoni, Federico;Petrosino, Lorenzo;Marchetti, Alessandro;Bacco, Luca; ...
Layered meta-learning algorithm for predicting adverse events in Type 1 Diabetes fully represent the complex rules lying behind the different glucose ...
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An ensemble-based machine learning model for predicting type 2 ...
pmc.ncbi.nlm.nih.gov › PMC11134939
May 29, 2024 · Ensemble based models XGboost and RF achieved over 84% accuracy for detecting diabetes. After applying RFE, we selected only 20 features which ...
Mar 30, 2024 · In this study, we developed machine learning (ML) and deep learning (DL) models to predict nocturnal glucose within the target range (3.9–10 ...