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 | |||
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Dr. Andreas Bender | Lead | |||
Dr. Susanne Dandl | PostDoc | |||
Dr. David Rügamer | PostDoc | |||
Lukas Burk | PhD Student | |||
Philipp Kopper | PhD Student | |||
Theresa Stüber | PhD Student | |||
Tobias Weber | PhD Student | |||
Florian Karl | PhD Student |
Publications
- 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.
link. - 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.
link. - Hendrix P, Sun CC, Brighton H, Bender A (2023) On the Connection Between Language Change and Language Processing. Cognitive Science 47, e13384.
link|pdf. - 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.
link. - Wiegrebe S, Kopper P, Sonabend R, Bischl B, Bender A (2023) Deep Learning for Survival Analysis: A Review.
link|pdf. - Ramjith J, Bender A, Roes KCB, Jonker MA (2022) Recurrent Events Analysis with Piece-Wise Exponential Additive Mixed Models. Statistical Modelling, 1471082X221117612.
link|pdf. - Dandl S, Bender A, Hothorn T (2022) Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests.
link. - 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. - 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.
link|pdf. - Sonabend R, Bender A, Vollmer S (2022) Avoiding C-hacking When Evaluating Survival Distribution Predictions with Discrimination Measures. Bioinformatics 38, 4178–4184.
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 Workshops, Deep Generative Models and Downstream Applications.
link|pdf. - 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.
link|pdf. - 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.
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. - 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, Scheipl F (2018) pammtools: Piece-wise exponential Additive Mixed Modeling tools. arXiv:1806.01042 [stat].
link| pdf. - 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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