most citedAutomatic identification of diagnosis from hospital discharge letters via weakly supervised Natural Language Processing

1 citations · 1 across the 4 of their papers we have counts for

collaborators

20 papers

stat.ME2026

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…

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…

cs.CL20261 cited

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…

stat.AP2026

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…