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Background: Type 1 Diabetes Mellitus (T1DM) is a widespread chronic disease in industrialized countries. Preventing blood glucose levels from exceeding the euglycaemic range would reduce the incidence of diabetes-related complications and improve the quality of life of subjects with T1DM. As a consequence, in the last decade, many Machine Learning algorithms aiming to forecast future blood glucose levels have been proposed. Despite the excellent performance they obtained, the prediction of abrupt changes in blood glucose values produced during physical activity (PA) is still one of the main challenges. Methods: A Jump Neural Network was developed in order to overcome the issue of predicting blood glucose values during PA. Three learning configurations were developed and tested: offline training, online training, and online training with reinforcement. All configurations were tested on six subjects suffering from T1DM that held regular PA (three aerobic and three anaerobic) and exploited Continuous Glucose Monitoring (CGM). Results: The forecasting performance was evaluated in terms of the Root-Mean-Squared-Error (RMSE), according to a paradigm of Precision Medicine. Conclusions: The online learning configurations performed better than the offline configuration in total days but not on the only CGM associated with the PA; thus, the results do not justify the increased computational burden because the improvement was not significant. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a widespread chronic disease and is one of the main causes of death worldwide. In order to improve the quality of life of people with diabetes and reduce the occurrence of complications, it is fundamental to prevent glycemic levels from exceeding the physiologic range. With this purpose, many works in recent years have been developed to forecast future glycemic trends using machine learning algorithms that exploit the reading of continuous glucose monitoring sensors, which gather glycemic data from diabetic patients 24 h a day. However, their application is limited in practice by the fact that they usually require a large amount of training data and other heterogeneous features gathered from patients. For this reason, in this work we present a novel neural network capable of predicting future glycemic levels using only the past glucose values as input while needing a small amount of training data. The model is a jump neural network with the addition of feedback connections from the output to the hidden layer, and time delays for each of the input-to-hidden, output-to-hidden and input-to-output connections. Experiments were conducted on a private and a public dataset. We evaluated performance in terms of RMSE and of adverse event detection. The proposed model outperforms other methods suited for time series forecasting, as well as models for blood glucose level prediction present in the literature. • A novel net named Auto-Regressive Time Delayed (ARTiDe) jump neural network is presented. • It is tested on blood glucose levels prediction using 24 h of monitoring data. • The model exploits a univariate approach reducing the burden of data collection. • The model outperforms many multivariate approaches at the state of the art. • A thorough overview of glucose levels forecasting is provided. [ABSTRACT FROM AUTHOR]

With the increasingly widespread adoption of Healthcare 4.0 practices, new challenges have arisen for the utilization of collected sensitive data. On the one hand, these data have immense potential to unlock valuable insights for personalized medicine, early disease detection, and predictive analysis thanks to the use of Artificial Intelligence. On the other hand, ensuring the protection of patient privacy is of paramount importance to maintain trust and uphold ethical practices within the healthcare system. Classical centralized learning approaches do not fit well with the privacy and security requirements imposed by the law and the sensitivity of treated data, which is why decentralized learning approaches are gaining ground. Among these, Federated Learning (FL) stands out as a viable alternative, providing greater security and performance comparable to classic centralized learning approaches. However, there are still various attacks targeting the local parameters or gradients updated by the participants. Therefore, we present our architecture based on the conjunction of Zero-Knowledge Proof, FL, and blockchain that implements also the decentralized identifier standard. The adoption of this architecture can grant the execution, management, supervision, and updating of the FL process, guaranteeing the resilience of the system and the reliability and traceability of exchanged data. In order to test the performance, robustness, and implementation costs of the proposed architecture, we develop a case study on the prediction of blood glucose levels in people with Type-1-diabetes. The results of our analysis show an improved system in terms of balance between performance privacy and security, guaranteeing high levels of verifiability, therefore proving the proposed architecture suitable for most of the FL processes needed in the healthcare field. • Incorporation of Zero-Knowledge-Proof Blockchains and Digital Identities. • System for Federated Learning Process Management. • Enhancement of Security, Privacy, and Auditability upon Adoption. • Facilitation of Communication among Federated Process Participants. [ABSTRACT FROM AUTHOR]