Multivariate scalar on multidimensional distribution regression
R Ghosal, M Matabuena - arXiv preprint arXiv:2310.10494, 2023 - arxiv.org
We develop a new method for multivariate scalar on multidimensional distribution
regression. Traditional approaches typically analyze isolated univariate scalar outcomes or …
regression. Traditional approaches typically analyze isolated univariate scalar outcomes or …
Deep learning framework with uncertainty quantification for survey data: Assessing and predicting diabetes mellitus risk in the american population
M Matabuena, JC Vidal, R Ghosal… - arXiv preprint arXiv …, 2024 - arxiv.org
Complex survey designs are commonly employed in many medical cohorts. In such
scenarios, developing case-specific predictive risk score models that reflect the unique …
scenarios, developing case-specific predictive risk score models that reflect the unique …
Control of medical digital twins with artificial neural networks
L Boetttcher, LL Fonseca, R Laubenbacher - bioRxiv, 2024 - biorxiv.org
The objective of personalized medicine is to tailor interventions to an individual patient's
unique characteristics. A key technology for this purpose involves medical digital twins …
unique characteristics. A key technology for this purpose involves medical digital twins …
Multivariate Scalar on Multidimensional Distribution Regression With Application to Modeling the Association Between Physical Activity and Cognitive Functions
R Ghosal, M Matabuena - Biometrical Journal, 2024 - Wiley Online Library
We develop a new method for multivariate scalar on multidimensional distribution
regression. Traditional approaches typically analyze isolated univariate scalar outcomes or …
regression. Traditional approaches typically analyze isolated univariate scalar outcomes or …
Uncertainty quantification in metric spaces
G Lugosi, M Matabuena - arXiv preprint arXiv:2405.05110, 2024 - arxiv.org
This paper introduces a novel uncertainty quantification framework for regression models
where the response takes values in a separable metric space, and the predictors are in a …
where the response takes values in a separable metric space, and the predictors are in a …
Uncertainty quantification for intervals
Data following an interval structure are increasingly prevalent in many scientific applications.
In medicine, clinical events are often monitored between two clinical visits, making the exact …
In medicine, clinical events are often monitored between two clinical visits, making the exact …