3 papers
stat.ML2026
Discovery and inference beyond linearity for epidemiological data by integrating Bayesian regression, tree ensembles and Shapley values
Giorgio Spadaccini, Marjolein Fokkema, Mark A. van de Wiel
Machine Learning (ML) is gaining popularity in epidemiology and healthcare studies for hypothesis-free discovery of risk and protective factors. ML is strong at discovering nonline…
stat.ME2024
Fusion of Tree-induced Regressions for Clinico-genomic Data
Jeroen M. Goedhart, Mark A. van de Wiel, Wessel N. van Wieringen +1
Cancer prognosis is often based on a set of omics covariates and a set of established clinical covariates such as age and tumor stage. Combining these two sets poses challenges. Fi…
stat.ME2024
Guiding adaptive shrinkage by co-data to improve regression-based prediction and feature selection
Mark A. van de Wiel, Wessel N. van Wieringen
The high dimensional nature of genomics data complicates feature selection, in particular in low sample size studies - not uncommon in clinical prediction settings. It is widely re…