most citedA latent class approach to assess the effects of dynamic adherence to polytherapy in heart failure patients

2 citations · 2 across the 3 of their papers we have counts for

collaborators

6 papers

stat.AP2026

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…

stat.AP2026

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…

stat.AP20262 cited

A latent class approach to assess the effects of dynamic adherence to polytherapy in heart failure patients

Nicole Fontana, Laura Savaré, Emanuele Di Angelantonio +1

Heart failure (HF) treatment relies heavily on pharmacotherapy, particularly combining multiple therapies as recommended by clinical guidelines. However, non-adherence to prescribe…

stat.CO2026

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…

cs.LG2026

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…

stat.AP2026

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…