Impact of nutritional factors in blood glucose prediction in type 1 diabetes through machine learning
G Annuzzi, A Apicella, P Arpaia, L Bozzetto… - IEEE …, 2023 - ieeexplore.ieee.org
Type 1 Diabetes (T1D) is an autoimmune disease that affects millions of people worldwide.
A critical issue in T1D patients is the managing of Postprandial Glucose Response (PGR) …
A critical issue in T1D patients is the managing of Postprandial Glucose Response (PGR) …
Toward the application of XAI methods in EEG-based systems
An interesting case of the well-known Dataset Shift Problem is the classification of
Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI). The …
Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI). The …
Exploring nutritional influence on blood glucose forecasting for Type 1 diabetes using explainable AI
G Annuzzi, A Apicella, P Arpaia… - IEEE journal of …, 2023 - ieeexplore.ieee.org
Type 1 diabetes mellitus (T1DM) is characterized by insulin deficiency and blood sugar
control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates …
control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates …
Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems
A central issue addressed by the rapidly growing research area of eXplainable Artificial
Intelligence (XAI) is to provide methods to give explanations for the behaviours of Machine …
Intelligence (XAI) is to provide methods to give explanations for the behaviours of Machine …
Predicting and monitoring blood glucose through nutritional factors in type 1 diabetes by artificial neural networks
G Annuzzi, L Bozzetto, A Cataldo, S Criscuolo… - Acta IMEKO, 2023 - acta.imeko.org
The monitoring and management of Postprandial Glucose Response (PGR), by
administering an insulin bolus before meals, is a crucial issue in Type 1 Diabetes (T1D) …
administering an insulin bolus before meals, is a crucial issue in Type 1 Diabetes (T1D) …
Middle-level features for the explanation of classification systems by sparse dictionary methods
Machine learning (ML) systems are affected by a pervasive lack of transparency. The
eXplainable Artificial Intelligence (XAI) research area addresses this problem and the …
eXplainable Artificial Intelligence (XAI) research area addresses this problem and the …
Strategies to exploit XAI to improve classification systems
Abstract Explainable Artificial Intelligence (XAI) aims to provide insights into the decision-
making process of AI models, allowing users to understand their results beyond their …
making process of AI models, allowing users to understand their results beyond their …
Contrastive explanations to classification systems using sparse dictionaries
Providing algorithmic explanations for the decisions of machine learning systems to end
users, data protection officers, and other stakeholders in the design, production …
users, data protection officers, and other stakeholders in the design, production …
[PDF][PDF] SHAP-based Explanations to Improve Classification Systems.
Abstract Explainable Artificial Intelligence (XAI) is a field usually dedicated to offering
insights into the decisionmaking mechanisms of AI models. Its purpose is to enable users to …
insights into the decisionmaking mechanisms of AI models. Its purpose is to enable users to …
A general approach to compute the relevance of middle-level input features
This work proposes a novel general framework, in the context of eXplainable Artificial
Intelligence (XAI), to construct explanations for the behaviour of Machine Learning (ML) …
Intelligence (XAI), to construct explanations for the behaviour of Machine Learning (ML) …