24 citations · 30 across the 10 of their papers we have counts for
4 papers · 1 filter
CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks
Alejandro Almodóvar, Patricia A. Apellániz, Santiago Zazo +1
Deep neural networks achieve state-of-the-art performance in estimating heterogeneous treatment effects, but their opacity limits trust and adoption in sensitive domains such as me…
Interpretable Clinical Classification with Kolmogorov-Arnold Networks
Alejandro Almodóvar, Patricia A. Apellániz, Alba Garrido +3
The increasing use of machine learning in clinical decision support has been limited by the lack of transparency of many high-performing models. In clinical settings, predictions m…
Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks
Alba Garrido, Alejandro Almodóvar, Patricia A. Apellániz +2
Accurate survival prediction is critical in oncology for prognosis and treatment planning. Traditional approaches often rely on a single data modality, limiting their ability to ca…
DeCaFlow: A deconfounding causal generative model
Alejandro Almodóvar, Adrián Javaloy, Juan Parras +2
We introduce DeCaFlow, a deconfounding causal generative model. Training once per dataset using just observational data and the underlying causal graph, DeCaFlow enables accurate c…