mlsanityData to 7 Oct 2026

Machine Learning for Survival Analysis: A Survey

arXiv 1708.04649 ↗First posted 15 Aug 2017cs.LG stat.MLcited by 112

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Abstract (arXiv)

Accurately predicting the time of occurrence of an event of interest is a critical problem in longitudinal data analysis. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during the monitoring period. Such a phenomenon is called censoring which can be effectively handled using survival analysis techniques. Traditionally, statistical approaches have been widely developed in the literature to overcome this censoring issue. In addition, many machine learning algorithms are adapted to effectively handle survival data and tackle other challenging problems that arise in real-world data. In this survey, we provide a comprehensive and structured review of the representative statistical methods along with the machine learning techniques used in survival analysis and provide a detailed taxonomy of the existing methods. We also discuss several topics that are closely related to survival analysis and illustrate several successful applications in various real-world application domains. We hope that this paper will provide a more thorough understanding of the recent advances in survival analysis and offer some guidelines on applying these approaches to solve new problems that arise in applications with censored data.

Key facts. Cited by 112 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Aug 2017.

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Citing papers per year: 2021: 8; 2022: 16; 2023: 21; 2024: 26; 2025: 24; 2026: 17.

112 papers.

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  1. 2023Deep Learning for Survival Analysis: A Reviewcited by 75
  2. 2023Cross-Modal Translation and Alignment for Survival Analysiscited by 70
  3. 2021SurvTRACE: Transformers for Survival Analysis with Competing Eventscited by 40
  4. 2024CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysiscited by 22
  5. 2022Longitudinal Fairness with Censorshipcited by 21
  6. 2023Neural Fine-Gray: Monotonic neural networks for competing riskscited by 20
  7. 2024An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomescited by 16
  8. 2022SurvSet: An open-source time-to-event dataset repositorycited by 15
  9. 2023Fair Decision-making Under Uncertaintycited by 15
  10. 2022Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographicscited by 14
  11. 2024Fairness in Survival Analysis with Distributionally Robust Optimizationcited by 13
  12. 2023Individual Fairness under Uncertaintycited by 11
38 more
  1. 2023Proper Scoring Rules for Survival Analysiscited by 9
  2. 2021T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLPcited by 8
  3. 2022Generalized Delayed Feedback Model with Post-Click Information in Recommender Systemscited by 8
  4. 2024SCMIL: Sparse Context-aware Multiple Instance Learning for Predicting Cancer Survival Probability Distribution in Whole Slide Imagescited by 8
  5. 2023Federated Survival Forestscited by 7
  6. 2023Weakly Supervised Regression with Interval Targetscited by 6
  7. 2023Modeling Events and Interactions through Temporal Processes -- A Surveycited by 5
  8. 2023Conformalized Deep Splines for Optimal and Efficient Prediction Setscited by 5
  9. 2026Tabular Foundation Models Can Do Survival Analysiscited by 5
  10. 2022Flexible Group Fairness Metrics for Survival Analysiscited by 4
  11. 2022A Federated Cox Model with Non-Proportional Hazardscited by 4
  12. 2023Heterogeneous Datasets for Federated Survival Analysis Simulationcited by 4
  13. 2024Optimal Survival Trees: A Dynamic Programming Approachcited by 4
  14. 2024Interpretable Machine Learning for Survival Analysiscited by 4
  15. 2024FPBoost: Fully Parametric Gradient Boosting for Survival Analysiscited by 4
  16. 2025NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantificationcited by 4
  17. 2021Computing the Hazard Ratios Associated with Explanatory Variables Using Machine Learning Models of Survival Datacited by 3
  18. 2021Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAscited by 3
  19. 2022Predicting Survival Outcomes in the Presence of Unlabeled Datacited by 3
  20. 2023Deep Clustering Survival Machines with Interpretable Expert Distributionscited by 3
  21. 2023Using Geographic Location-based Public Health Features in Survival Analysiscited by 3
  22. 2023SurvBeX: An explanation method of the machine learning survival models based on the Beran estimatorcited by 3
  23. 2024Generating Survival Interpretable Trajectories and Datacited by 3
  24. 2024Advancing Head and Neck Cancer Survival Prediction via Multi-Label Learning and Deep Model Interpretationcited by 3
  25. 2024Clustering Survival Data using a Mixture of Non-parametric Expertscited by 3
  26. 2024A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Datacited by 3
  27. 2024Random Survival Forest for Censored Functional Datacited by 3
  28. 2026SVL: Goal-Conditioned Reinforcement Learning as Survival Learningcited by 3
  29. 2023Multimodal Deep Learning for Personalized Renal Cell Carcinoma Prognosis: Integrating CT Imaging and Clinical Datacited by 2
  30. 2023Dual-stage optimizer for systematic overestimation adjustment applied to multi-objective genetic algorithms for biomarker selectioncited by 2
  31. 2024Experimental Comparison of Ensemble Methods and Time-to-Event Analysis Models Through Integrated Brier Score and Concordance Indexcited by 2
  32. 2024Interpretable Prediction and Feature Selection for Survival Analysiscited by 2
  33. 2025SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysiscited by 2
  34. 2025Competing Risks: Impact on Risk Estimation and Algorithmic Fairnesscited by 2
  35. 2021Boost-R: Gradient Boosted Trees for Recurrence Datacited by 1
  36. 2021EsmamDS: A more diverse exceptional survival model mining approachcited by 1
  37. 2022BELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractorcited by 1
  38. 2022Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selectioncited by 1

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