1 citations · 1 across the 6 of their papers we have counts for
7 papers
HyperNSDE: Personalized Neural SDEs for Joint Static-Longitudinal Clinical Data Generation
Perrine Chassat, Agathe Guilloux
Synthetic patient data generation is a promising solution to the dual challenge of data scarcity and privacy constraints in healthcare machine learning. Realistic synthesis of pati…
Latent Neural-ODE for Model-Informed Precision Dosing: Overcoming Structural Assumptions in Pharmacokinetics
Benjamin Maurel, Agathe Guilloux, Sarah Zohar +2
Accurate estimation of tacrolimus exposure, quantified by the area under the concentration-time curve (AUC), is essential for precision dosing after renal transplantation. Current…
Toward Valid Generative Clinical Trial Data with Survival Endpoints
Perrine Chassat, Van Tuan Nguyen, Lucas Ducrot +2
Clinical trials face mounting challenges: fragmented patient populations, slow enrollment, and unsustainable costs, particularly for late phase trials in oncology and rare diseases…
Calibrating confounding strength in sensitivity models for weighting estimators: a comparative review and a new method
Jean-Baptiste Baitairian, Bernard Sebastien, Rana Jreich +2
Causal inference is only valid when its underlying assumptions are satisfied, one of the most central being the ignorability or unconfoundedness assumption. However, this hypothesi…
A regularized multi-state model for covariate selection with interval-censored survival data
Ariane Bercu, Agathe Guilloux, Cécile Proust-Lima +1
In population-based cohorts, disease diagnoses are typically censored by intervals as made during scheduled follow-up visits. The exact disease onset time is thus unknown, and in t…
Sharp Bounds for Continuous-Valued Treatment Effects with Unobserved Confounders
Jean-Baptiste Baitairian, Bernard Sebastien, Rana Jreich +2
In causal inference, treatment effects are typically estimated under the ignorability, or unconfoundedness, assumption, which is often unrealistic in observational data. By relaxin…
Dynamical Survival Analysis with Controlled Latent States
Linus Bleistein, Van-Tuan Nguyen, Adeline Fermanian +1
We consider the task of learning individual-specific intensities of counting processes from a set of static variables and irregularly sampled time series. We introduce a novel mode…