On Training Survival Models with Scoring Rules

2026
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Authors: Philipp Kopper; David Rügamer; Raphael Sonabend; Bernd Bischl; Andreas Bender
Published in: Machine Learning and Knowledge Discovery in Databases. Research Track
Publisher: Springer Nature Switzerland

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Abstract

This chapter delves into the application of scoring rules for training survival models, a groundbreaking approach that addresses the challenges of censored data and competing risks. The authors present a framework that is model-agnostic, allowing for parametric and non-parametric estimation of event time distributions. The text explores the use of various scoring rules, such as the integrated survival brier score (ISBS) and its proper alternatives, and demonstrates their effectiveness through empirical evaluations on synthetic and real-world datasets. The authors also compare their method with established state-of-the-art survival models, showing competitive predictive performance. Additionally, the chapter discusses the implementation of the framework in different machine learning models, including neural networks, gradient boosting, trees, and generalized additive modeling. The findings highlight the versatility and robustness of the proposed method, making it a significant advancement in the field of survival analysis.

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Metadata
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Title: On Training Survival Models with Scoring Rules
Authors: Philipp Kopper
David Rügamer
Raphael Sonabend
Bernd Bischl
Andreas Bender
Publisher: Springer Nature Switzerland
Book: Machine Learning and Knowledge Discovery in Databases. Research Track
Print ISBN: 978-3-032-06108-9

Electronic ISBN: 978-3-032-06109-6

Copyright Year: 2026
DOI: https://doi.org/10.1007/978-3-032-06109-6_5

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