3 papers
stat.ME2026
Distinguishing case-mix from context heterogeneity in prognostic regression model synthesis settings
Max Behrens, Janis M. Nolde, Eleni Papakonstantinou +6
Prognostic regression models often synthesize data from multiple sites, whether within a multi-site study, across federated settings, or in individual participant data meta-analysi…
cs.LG2026
Eliciting associations between clinical variables from LLMs via comparison questions across populations
Fabian Kabus, Kian Kordtomeikel, Thomas Brox +3
The training data of large language models (LLMs) comprises a wide range of biomedical literature, reflecting data from many different patient populations. We investigate how it mi…
stat.ME2026
Contrasting Global and Patient-Specific Regression Models via a Neural Network Representation
Max Behrens, Daiana Stolz, Eleni Papakonstantinou +5
When developing clinical prediction models, it can be challenging to balance between global models that are valid for all patients and personalized models tailored to individuals o…