PUB - Publikationen an der Universität Bielefeld

In medical pathology, tumor grading is concerned with estimating the risk posed by a tumor, based on its pathological features. One way to infer risk scores is survival regression, i.e. using machine learning to infer a score that predicts the remaining survival time of a patient. Unfortunately, if applied naively, such a score is a mix of the intrinsic risk posed by the tumor and other risk factors, like the progression of the tumor or patient gender and age. We provide the first survival regression model that disentangles tumor grading from undesired correlations, while retaining a high degree of model interpretability, thanks to convex optimization, non-negativity constraints, sparsity, and linearity. We evaluate the proposed approach both on simulated and real-world data from N = 114 patients at the University Clinic Aachen.