Interpretable approaches for decorrelated sparse survival regression

The risk posed by a tumor can be described on two axes: how far the tumor has already grown and spread in the body (staging) and how dangerous/(de-)differentiated the tumor is irrespective of staging due to its pathological features (grading). When we apply survival analysis to cancer data, we implicitly mix staging and grading. Our goal is to develop novel survival analysis approaches to support medical experts in deriving a grading scheme from data. Hence, we need models that are small and sparse enough for medical experts to analyze, take bio-medical constraints into account, are still predictive of survival, and, most importantly, are decorrelated from staging indicators. Learning such models from small data is a novel machine learning problem that we dub decorrelated sparse survival regression (DSSR). In this paper, we provide three algorithms for DSSR, develop further mechanisms for model interpretability, and evaluate all algorithms on both simulated and real-world cancer data. • We define the machine learning problem of decorrelated sparse survival regression (DSSR) with applications in tumor grading. • We provide three novel algorithms to address the problem. • We evaluate the algorithms on both simulated and real-world data and find substantial improvements beyond prior algorithms, especially in terms of interpretability.