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. John Zobolas       Postdoc
Lukas Burk       PhD Student
Johannes Piller       PhD Student
Simon Wiegrebe       PhD Student
Philipp Kopper       PhD Student
Theresa Stüber       PhD Student
Sophie Langbein       PhD Student
Lea Orsini       PhD Student

Publications

  1. Kopper P, Rügamer D, Sonabend R, Bischl B, Bender A (2024) Training Survival Models using Scoring Rules.
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  2. Dandl S, Haslinger C, Hothorn T, Seibold H, Sverdrup E, Wager S, Zeileis A (2024) What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work? The Annals of Applied Statistics 18, 506–528.
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  3. Dandl S, Bender A, Hothorn T (2024) Heterogeneous treatment effect estimation for observational data using model-based forests. Statistical Methods in Medical Research 33, 392–413.
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  4. Wiegrebe S, Kopper P, Sonabend R, Bischl B, Bender A (2024) Deep Learning for Survival Analysis: A Review. Artificial Intelligence Review 57, 65.
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  5. Zumeta-Olaskoaga L, Bender A, Lee D-J (2024) Flexible Modelling of Time-Varying Exposures and Recurrent Events to Analyse Training Load Effects in Team Sports Injuries. Journal of the Royal Statistical Society Series C: Applied Statistics, qlae059.
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  6. Hartl WH, Kopper P, Xu L, Heller L, Mironov M, Wang R, Day AG, Elke G, Küchenhoff H, Bender A (2023) Relevance of Protein Intake for Weaning in the Mechanically Ventilated Critically Ill: Analysis of a Large International Database. Critical Care Medicine.
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  7. Hendrix P, Sun CC, Brighton H, Bender A (2023) On the Connection Between Language Change and Language Processing. Cognitive Science 47, e13384.
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  8. Coens F, Knops N, Tieken I, Vogelaar S, Bender A, Kim JJ, Krupka K, Pape L, Raes A, Tönshoff B, Prytula A, Registry C (2023) Time-Varying Determinants of Graft Failure in Pediatric Kidney Transplantation in Europe. Clinical Journal of the American Society of Nephrology.
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  9. Ramjith J, Bender A, Roes KCB, Jonker MA (2022) Recurrent Events Analysis with Piece-Wise Exponential Additive Mixed Models. Statistical Modelling, 1471082X221117612.
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  10. 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.
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  11. Hartl WH, Kopper P, Bender A, Scheipl F, Day AG, Elke G, Küchenhoff H (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.
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  12. Pretzsch E, Heinemann V, Stintzing S, Bender A, Chen S, Holch JW, Hofmann FO, Ren H, Bösch F, Küchenhoff H, Werner J, Angele MK (2022) EMT-Related Genes Have No Prognostic Relevance in Metastatic Colorectal Cancer as Opposed to Stage II/III: Analysis of the Randomised, Phase III Trial FIRE-3 (AIO KRK 0306; FIRE-3). Cancers 14, 5596.
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  13. Sonabend R, Bender A, Vollmer S (2022) Avoiding C-hacking When Evaluating Survival Distribution Predictions with Discrimination Measures. Bioinformatics 38, 4178–4184.
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  14. Weber T, Ingrisch M, Bischl B, Rügamer D (2021) Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. NeurIPS 2021 Workshops, Deep Generative Models and Downstream Applications.
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  15. Weber T, Ingrisch M, Fabritius M, Bischl B, Rügamer D (2021) Survival-oriented embeddings for improving accessibility to complex data structures. NeurIPS 2021 Workshops, Bridging the Gap: From Machine Learning Research to Clinical Practice.
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  16. Fabritius MP, Seidensticker M, Rueckel J, Heinze C, Pech M, Paprottka KJ, Paprottka PM, Topalis J, Bender A, Ricke J, Mittermeier A, Ingrisch M (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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  17. 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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  18. 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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  19. 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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  20. Bender A, Scheipl F (2018) pammtools: Piece-wise exponential Additive Mixed Modeling tools. arXiv:1806.01042 [stat].
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  21. Burk L, Zobolas J, Bischl B, Bender A, Wright MN, Sonabend R (2024) A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data.
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  22. Stüber AT, Coors S, Schachtner B, Weber T, Rügamer D, Bender A, Mittermeier A, Öcal O, Seidensticker M, Ricke J, others (2023) A Comprehensive Machine Learning Benchmark Study for Radiomics-Based Survival Analysis of CT Imaging Data in Patients With Hepatic Metastases of CRC. Investigative Radiology, 10–1097.
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