Machine Learning for Survival Analysis

This group focuses on methodological and applied research in the context of survival analysis (SA). Topics include

• Deep SA for multi-modal data types

• Methodological frameworks for SA in machine learning

• Applied (deep) SA in medical applications

• Counterfactual explanations

• Trees, forests and boosting for SA

• Generative survival models

Members

Publications

1. Kopper P, Wiegrebe S, Bischl B, Bender A, Rügamer D (2022) DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis Advances in Knowledge Discovery and Data Mining, pp. 249–261. Springer International Publishing.
   link|pdf.
2. Hartl WH, Kopper P, Bender A et al. (2022) Protein intake and outcome of critically ill patients: analysis of a large international database using piece-wise exponential additive mixed models. Critical Care 26, 7.
   link|pdf.
3. Weber T, Ingrisch M, Fabritius M, Bischl B, Rügamer D (2021) Survival-oriented embeddings for improving accessibility to complex data structures. NeurIPS 2021, Bridging the Gap: From Machine Learning Research to Clinical Practice.
   link|pdf.
4. Weber T, Ingrisch M, Bischl B, Rügamer D (2021) Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. NeurIPS 2021, Deep Generative Models and Downstream Applications.
   link|pdf.
5. Fabritius MP, Seidensticker M, Rueckel J et al. (2021) Bi-Centric Independent Validation of Outcome Prediction after Radioembolization of Primary and Secondary Liver Cancer. Journal of Clinical Medicine 10, 3668.
   link|pdf.
6. Ramjith J, Bender A, Roes KCB, Jonker MA (2021) Recurrent Events Analysis with Piece-wise exponential Additive Mixed Models. Research Square.
   link|pdf.
7. Kopper P, Pölsterl S, Wachinger C, Bischl B, Bender A, Rügamer D (2021) Semi-Structured Deep Piecewise Exponential Models. In: In: Greiner R , In: Kumar N , In: Gerds TA , In: Schaar M van der (eds) Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, pp. 40–53. PMLR.
   link|pdf.
8. Bender A, Rügamer D, Scheipl F, Bischl B (2021) A General Machine Learning Framework for Survival Analysis. In: In: Hutter F , In: Kersting K , In: Lijffijt J , In: Valera I (eds) Machine Learning and Knowledge Discovery in Databases, pp. 158–173. Springer International Publishing.
   link | pdf.
9. Sonabend R, Király FJ, Bender A, Bischl B, Lang M (2021) mlr3proba: An R Package for Machine Learning in Survival Analysis. Bioinformatics.
   link|pdf.
10. Bender A, Scheipl F (2018) pammtools: Piece-wise exponential Additive Mixed Modeling tools. arXiv:1806.01042 [stat].
    link| pdf.