BALELLI , Irene

The multimodal nature of clinical assessment and decision-making, and the high rate of healthcare data generation, motivate the need to develop approaches specifically adapted to the analysis of these complex and potentially high-dimensional multimodal datasets. This poses both technical and conceptual problems: how can such heterogeneous data...

We propose a novel federated learning paradigm to model data variability among heterogeneous clients in multi-centric studies. Our method is expressed through a hierarchical Bayesian latent variable model, where client-specific parameters are assumed to be realization from a global distribution at the master level, which is in turn estimated to...

We propose a novel federated learning paradigm to model data variability among heterogeneous clients in multi-centric studies. Our method is expressed through a hierarchical Bayesian latent variable model, where client-specific parameters are assumed to be realization from a global distribution at the master level, which is in turn estimated to...

We present a parameter estimation method for nonlinear mixed effectmodels based on ordinary differential equations (NLME-ODEs). The methodpresented here aims at regularizing the estimation problem in presenceof model misspecifications, practical identifiability issues and unknowninitial conditions. For doing so, we define our estimator as the...

We present a parameter estimation method for nonlinear mixed effectmodels based on ordinary differential equations (NLME-ODEs). The methodpresented here aims at regularizing the estimation problem in presenceof model misspecifications, practical identifiability issues and unknowninitial conditions. For doing so, we define our estimator as the...

The goal of this chapter is to provide a gentle introduction to Causal Learning (CL), and motivation for its application to medical image analysis, seeking for more robustness against data and domain drifts, and a reliable tool to answer conterfactuals questions and get improved interpretability. The probabilistic formalism at the basis of CL...

Federated learning allows for the training of machine learn- ing models on multiple decentralized local datasets without requiring explicit data exchange. However, data pre-processing, including strate- gies for handling missing data, remains a major bottleneck in real-world federated learning deployment, and is typically performed locally....

This chapter focuses on the joint modeling of heterogeneous information, such as imaging, clinical, and biological data. This kind of problem requires to generalize classical uni-and multivariate association models to account for complex data structure and interactions, as well as high data dimensionality. Typical approaches are essentially...

Pacemakers are commonly required to treat bradycardia. They are composed of a pulse generator and leads implanted in the heart, and deliver an electrical pulse so as to elicit cardiac contraction. The capture threshold (minimum energy required to stimulate the heart) is critical to assess and predict pacemaker performance. Indeed, the threshold...

Knowing the impact of causal relationships between ion channelblockade and electromechanical biomarkers is essential to improve drug-induced torsades de pointes (TdP)-risk assessment. Apart from commonpurely electric torsadogenic indices, mechanical biomarkers may provideadditional proarrhythmic information, but the impact and...

Drug-induced Torsade de pointes (TdP) is a critical arrhythmia that can lead to sudden cardiac death. Besides ionic current blockades, in-silico electrophysiological and mechanical biomarkers can provide mechanistic proarrhythmic information for TdP-risk assessment, and specific torsadogenic indices have been developed for that purpose, yet,...

Drug-induced Torsade de pointes (TdP) is a critical arrhythmia that can lead to sudden cardiac death. Recently, in-silico electrophysiological models have emerged as essential tools to predict the drug effects on cardiac activity. These models, along with biomarkers and torsadogenic indices can provide mechanistic insights for TdP-risk...

The query of causality is of paramount importance in biomedical data analysis: assessing the causal relationships between the observed variables allows to improve our understanding of the tackled medical condition and better support decision-making. Torsade de Pointes (TdP) is an extremely serious drug-induced cardiac side effect, which can...