arXiv subjects

Alessia Paglialonga

Publications and source records attributed to Alessia Paglialonga.

6 recordsLinked to original sources

arXiv2026

Data Standards in Audiology: A Mixed-Methods Exploration of Community Perspectives and Implementation Considerations

Objective: This study addresses conceptual issues around data standardisation in audiology, and outlines steps toward achieving it. It reports a survey of the computational audiology community on their current understanding, needs, and preferences concerning data standards. Based on survey findings and a panel discussion, recommendations are made concerning moving forward with standardisation in audiology. Design: Mixed-methods: 1) review of existing standardisation efforts; 2) a survey of the computational audiology community; 3) expert panel discussion in a dedicated session at the 2024 Virtual Conference of Computational Audiology. Sample: Survey: 82 members of the global community; Panel discussion: five experts. Results: A prerequisite for any global audiology database are agreed data standards. Although many are familiar with the general idea, few know of existing initiatives, or have actively participated in them. Ninety percent of respondents expressed willingness to follow or contribute to standardisation efforts. The panel discussed relevant initiatives (e.g. OMOP, openEHR, Noah) and explored both challenges (around harmonisation) and opportunities (alignment with other medical fields and conversion among approaches). Conclusions: Combining conceptual discussion with stakeholder views, the study offers guidance for implementing interoperable data standards in audiology. It highlights community support, key issues to address, and suggests paths for future work.

cs.SD↗

arXiv2025

A reduced model for the long-term effects of physical activity on type 2 diabetes

Type 2 diabetes progresses slowly and may be reversed through lifestyle changes, but quantifying the long-term impact of regular physical activity remains challenging due to sparse longitudinal data. Mechanistic models offer a powerful tool by simulating metabolic processes over extended timescales. However, multi-scale formulations that capture both the short-term effects of exercise sessions and the slow evolution of disease tend to be computationally demanding, limiting their practical use in personalized decision support. To address this limitation, we derived a reduced version of a two-scale model that captures the short- and long-term effects of physical activity on blood glucose regulation. By analytically averaging the short-term effects induced by exercise, we developed a homogenized formulation that transmits the average contribution of physical activity to the slower glucose-insulin dynamics. This reduction preserves the key model dynamics while decreasing computational complexity by almost a factor 2000. We prove that the approximation error remains bounded and confirm the model's accuracy through a parameter-based simulation study. The resulting model provides a mathematically grounded reduction that retains key physiological mechanisms while enabling fast long-term simulations. This substantial computational gain makes it suitable for integration into medical decision support systems, where it can be used to design and evaluate personalized physical activity plans aimed at reducing the risk of type 2 diabetes.

q-bio.QM↗

arXiv2025

A Model-based Approach for Glucose Control via Physical Activity

The role played by physical activity in slowing down the progression of type-2 diabetes is well recognized. However, except for general clinical guidelines, quantitative real-time estimates of the recommended amount of physical activity, based on the evolving individual conditions, are {still missing} in the literature. The aim of this work is to provide a control-theoretical formulation of the exercise encoding all the exercise-related features (intensity, duration, period). Specifically, we design a feedback law in terms of recommended physical activity, following a model predictive control approach, based on a widespread compact diabetes progression model, suitably modified to account for the long-term effects of regular exercise. Preliminary simulations show promising results, well aligned with clinical evidence. These findings can be the basis for further validation of the control law on high-dimensional diabetes progression models to ultimately translate the predictions of the controller into meaningful recommendations.

eess.SY↗

arXiv2025

Closed-loop robust control of long-term diabetes progression via physical activity management

Large clinical evidence acknowledges the crucial role played by physical activity in delaying the progression of type-2 diabetes. However, the literature lacks control approaches that leverage exercise for type-2 diabetes control and more in general lacks a quantitative assessment of medical guidelines on the recommended amount of physical activity to be performed, mainly due to the absence of mathematical models that suitably estimate its benefits on diabetes progression. In this work, in order to provide a control-theoretical formulation of the exercise, we design a feedback law in terms of recommended physical activity, following a model predictive control approach, based on a widespread compact diabetes progression model, suitably modified to properly account for the long-term effect of the exercise. Moreover we illustrate how the proposed approach proves to show reliable robustness properties with respect to initial conditions and parameter perturbations, which may be used to reflect inter-patient variability. Results are encouraging in view of the validation of the control law on comprehensive high-dimensional models of diabetes progression, with the aim of translating the prediction of the controller into reasonable recommendations and to quantitatively support medical decision-making.

eess.SY↗

arXiv2025

A control system framework for counterfactuals: an optimization based approach

Counterfactuals are a concept inherited from the field of logic and in general attain to the existence of causal relations between sentences or events. In particular, this concept has been introduced also in the context of interpretability in artificial intelligence, where counterfactuals refer to the minimum change to the feature values that changes the prediction of a classification model. The artificial intelligence framework of counterfactuals is mostly focused on machine learning approaches, typically neglecting the physics of the variables that determine a change in class. However, a theoretical formulation of counterfactuals in a control system framework - i.e., able to account for the mechanisms underlying a change in class - is lacking. To fill this gap, in this work we propose an original control system, physics-informed, theoretical foundation for counterfactuals, by means of the formulation of an optimal control problem. We apply the proposed methodology to a general glucose-insulin regulation model and results appear promising and pave the way to the possible integration with artificial intelligence techniques, with the aim of feeding machine learning models with the physics knowledge acquired through the system framework.

eess.SY↗

arXiv2024

A novel mathematical model for predicting the benefits of physical activity on type 2 diabetes progression

Despite the well-acknowledged benefits of physical activity for type 2 diabetes (T2D) prevention, the literature surprisingly lacks validated models able to predict the long-term benefits of exercise on T2D progression and support personalized risk prediction and prevention. To bridge this gap, we developed a novel mathematical model that formalizes the link between exercise and short- and long-term glucose-insulin dynamics to predict the benefits of regular exercise on T2D progression. The model quantitatively captured the dose-response relationship (larger benefits with increasing intensity and/or duration of exercise), it consistently reproduced the benefits of clinical guidelines for diabetes prevention, and it accurately predicted persistent benefits following interruption of physical activity, in line with real-world evidence from the literature. These results are encouraging and can be the basis for future development of decision support tools able to assist patients and clinicians in tailoring preventive lifestyle interventions.

eess.SY↗