Machine Learning for Survival Analysis

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

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

  1. 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.
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  2. 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.
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  3. 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.
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  4. Ramjith J, Bender A, Roes KCB, Jonker MA (2021) Recurrent Events Analysis with Piece-wise exponential Additive Mixed Models. Research Square.
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  5. 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.
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  6. 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.
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  7. Sonabend R, Király FJ, Bender A, Bischl B, Lang M (2021) mlr3proba: An R Package for Machine Learning in Survival Analysis. Bioinformatics.
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  8. Bender A, Scheipl F (2018) pammtools: Piece-wise exponential Additive Mixed Modeling tools. arXiv:1806.01042 [stat].
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