Machine Learning for Survival Analysis
This group focuses on methodological and applied research in the context of survival analysis (SA). Topics include
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Deep SA for multi-modal data types
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Methodological frameworks for SA in machine learning
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Applied (deep) SA in medical applications
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Counterfactual explanations
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Trees, forests and boosting for SA
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Generative survival models
Members
| Name | Position | |||
|---|---|---|---|---|
| Dr. Andreas Bender | Lead | |||
| Dr. David Rügamer | PostDoc | |||
| Lukas Burk | PhD Student | |||
| Susanne Dandl | PhD Student | |||
| Philipp Kopper | PhD Student | |||
| Theresa Stüber | PhD Student | |||
| Tobias Weber | PhD Student | |||
| Florian Karl | PhD Student |
Publications
- 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. - 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. - 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. - Ramjith J, Bender A, Roes KCB, Jonker MA (2021) Recurrent Events Analysis with Piece-wise exponential Additive Mixed Models. Research Square.
link|pdf. - 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. - 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. - Sonabend R, Király FJ, Bender A, Bischl B, Lang M (2021) mlr3proba: An R Package for Machine Learning in Survival Analysis. Bioinformatics.
link|pdf. - Bender A, Scheipl F (2018) pammtools: Piece-wise exponential Additive Mixed Modeling tools. arXiv:1806.01042 [stat].
link| pdf.