6 citations · 9 across the 4 of their papers we have counts for
4 papers
A Causal Foundation Model for Structure and Outcome Prediction
Max Zhu, Martino Mansoldo, Ching-Hao Wang +1
We introduce TabPFN-CFM, a causal foundation model that can handle multiple causal problems. TabPFN-CFM predicts both causal structure and outcomes from observational data, support…
SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction
Intae Moon, Stefan Groha, Alexander Gusev
Effective learning from electronic health records (EHR) data for prediction of clinical outcomes is often challenging because of features recorded at irregular timesteps and loss t…
Topological Data Analysis of copy number alterations in cancer
Stefan Groha, Caroline Weis, Alexander Gusev +1
Identifying subgroups and properties of cancer biopsy samples is a crucial step towards obtaining precise diagnoses and being able to perform personalized treatment of cancer patie…
A General Framework for Survival Analysis and Multi-State Modelling
Stefan Groha, Sebastian M Schmon, Alexander Gusev
Survival models are a popular tool for the analysis of time to event data with applications in medicine, engineering, economics, and many more. Advances like the Cox proportional h…