16 citations · 49 across the 38 of their papers we have counts for
39 papers
Generalized propensity score weighting for functional causal inference framework
Simone Ciardulli, Nicole Fontana, Simone Vantini +1
Estimating causal effects in observational studies requires adjustment for confounding, a task that becomes challenging when the exposure is a function observed over a continuous d…
Enhancing comorbidity network inference with risk-enriched health trajectories embedding
Nicole Fontana, Alessia Mapelli, Emanuele Di Angelantonio +1
Multimorbidity poses a growing challenge for individual health, reducing quality of life and increasing treatment burden, resulting in a multiplicative impact on healthcare system…
Prior-informed conditional Gaussian graphical models: an application to protein interaction network reconstruction
Alessia Mapelli, Michela Carlotta Massi, Gianmauro Cuccuru +2
Protein-protein interaction (PPI) networks, estimated from high-throughput omics data, foster biomarker discovery and precision medicine. Gaussian graphical models (GGMs) offer a p…
mmid: Multi-Modal Integration and Downstream analyses for healthcare analytics in Python
Andrea Mario Vergani, Valeria Iapaolo, Emanuele Di Angelantonio +2
mmid (Multi-Modal Integration and Downstream analyses for healthcare analytics) is a Python package that offers multi-modal fusion and imputation, classification, time-to-event pre…
SNPgen: Phenotype-Supervised Genotype Representation and Synthetic Data Generation via Latent Diffusion
Andrea Lampis, Michela Carlotta Massi, Nicola Pirastu +3
Polygenic risk scores and other genomic analyses require large individual-level genotype datasets, yet strict data access restrictions impede sharing. Synthetic genotype generation…
Trajectory-informed graph-based clustering for longitudinal cancer subtyping
Lara Cavinato, Marco Rocchi, Luca Viganò +1
Cancer subtyping plays a crucial role in informing prognosis and guiding personalized treatment strategies. However, conventional subtyping approaches often rely on static, biopsy-…
Hidden multistate models to study multimorbidity trajectories
Valentina Manzoni, Francesca Ieva, Amaia Calderón-Larrañaga +2
Multimorbidity in older adults is common, heterogeneous, and highly dynamic, and it is strongly associated with disability and increased healthcare utilization. However, existing a…
Penalized Likelihood Optimization for Adaptive Neighborhood Clustering in Time-to-Event Data with Group-Level Heterogeneity
Alessandra Ragni, Lara Cavinato, Francesca Ieva
The identification of patient subgroups with comparable event-risk dynamics plays a key role in supporting informed decision-making in clinical research. In such settings, it is im…
A neighbour selection approach for identifying differential networks in conditional functional graphical models
Alessia Mapelli, Laura Carini, Francesca Ieva +1
Estimating how different brain regions communicate with each other using EEG data is valuable both for medical research and clinical diagnosis. This involves quantifying the statis…
Multi-state Modeling of Delay Evolution in Suburban Rail Transports
Stefania Colombo, Alfredo Gimenez Zapiola, Francesca Ieva +1
Train delays are a persistent issue in railway systems, particularly in suburban networks where operational complexity is heightened by frequent services and high passenger volumes…
Unraveling time-varying causal effects of multiple exposures: integrating Functional Data Analysis with Multivariable Mendelian Randomization
Nicole Fontana, Francesca Ieva, Luisa Zuccolo +2
Mendelian Randomization is a widely used instrumental variable method for assessing causal effects of lifelong exposures on health outcomes. Many exposures, however, have causal ef…
Modeling group heterogeneity in spatio-temporal data via physics-informed semiparametric regression
Marco F. De Sanctis, Eleonora Arnone, Francesca Ieva +1
In this work we propose a novel approach for modeling spatio-temporal data characterized by group structures. In particular, we extend classical mixed effect regression models by i…
Scanner-Agnostic MRI Harmonization via SSIM-Guided Disentanglement
Luca Caldera, Lara Cavinato, Francesca Ieva
The variability introduced by differences in MRI scanner models, acquisition protocols, and imaging sites hinders consistent analysis and generalizability across multicenter studie…
Three Distributional Approaches for PM10 Assessment in Northern Italy
Marco F. De Sanctis, Andrea Gilardi, Giacomo Milan +3
We propose three spatial methods for estimating the full probability distribution of PM10 concentrations, with the ultimate goal of assessing air quality in Northern Italy. Moving…
Cluster-weighted modeling of lifetime hierarchical data for profiling COVID-19 heart failure patients
Luca Caldera, Andrea Cappozzo, Chiara Masci +5
This study investigates the heterogeneity in survival times among COVID-19 patients with Heart Failure (HF) hospitalized in the Lombardy region of Italy during the pandemic. To add…
Interpretable phenotyping of Heart Failure patients with Dutch discharge letters
Vittorio Torri, Machteld J. Boonstra, Marielle C. van de Veerdonk +6
Objective: Heart failure (HF) patients present with diverse phenotypes affecting treatment and prognosis. This study evaluates models for phenotyping HF patients based on left vent…
DISARM++: Beyond scanner-free harmonization
Luca Caldera, Lara Cavinato, Alessio Cirone +9
Harmonization of T1-weighted MR images across different scanners is crucial for ensuring consistency in neuroimaging studies. This study introduces a novel approach to direct image…
An Unsupervised Natural Language Processing Pipeline for Assessing Referral Appropriateness
Vittorio Torri, Annamaria Bottelli, Michele Ercolanoni +2
Objective: Assessing the appropriateness of diagnostic referrals is critical for improving healthcare efficiency and reducing unnecessary procedures. However, this task becomes cha…
Automatic identification of diagnosis from hospital discharge letters via weakly supervised Natural Language Processing
Vittorio Torri, Elisa Barbieri, Anna Cantarutti +2
Identifying patient diagnoses from hospital discharge letters is essential for large-scale cohort selection and epidemiological research, but traditional supervised approaches requ…
Uncover mortality patterns and hospital effects in COVID-19 heart failure patients: a novel Multilevel logistic cluster-weighted modeling approach
Luca Caldera, Chiara Masci, Andrea Cappozzo +4
Evaluating hospitals' performance and its relation to patients' characteristics is of utmost importance to ensure timely, effective, and optimal treatment. Such a matter is particu…
Integrating state-sequence analysis to uncover dynamic drug-utilization patterns to profile heart failure patients
Nicole Fontana, Laura Savaré, Francesca Ieva
Globally, the incidence of heart failure is increasing, and its principal treatment involves drug therapy. However, widespread non-adherence to therapies is prevalent among heart f…
Estimation of Dynamic Origin-Destination Matrices in a Railway Transportation Network integrating Ticket Sales and Passenger Count Data
Greta Galliani, Piercesare Secchi, Francesca Ieva
Accurately estimating Origin-Destination (OD) matrices is a topic of increasing interest for efficient transportation network management and sustainable urban planning. Traditional…
Joint modelling of recurrent and terminal events with discretely-distributed non-parametric frailty: application on re-hospitalizations and death in heart failure patients
Chiara Masci, Marta Spreafico, Francesca Ieva
In the context of clinical and biomedical studies, joint frailty models have been developed to study the joint temporal evolution of recurrent and terminal events, capturing both t…
Dynamic treatment effect phenotyping through functional survival analysis
Caterina Gregorio, Giovanni Baj, Giulia Barbati +1
In recent years, research interest in personalised treatments has been growing. However, treatment effect heterogeneity and possibly time-varying treatment effects are still often…