A list of papers/resources in Survival Analysis that I have read or would like to read. Should you wish to suggest an addition to this list, please feel free to open an issue.
Last Update Time: 2024.10.29
- Categories
- Tutorials/Surveys
- ML/DL Survival Models
- Objective Functions
- Time-varying Covariates Models
- Explainable Survival Models
- Competing Risks and Multi-Event Models
- Generalized Survival Analysis Methods
- Evaluation Metrics
- Causal Inference
- Fairness
- Dependent Censoring
- Synthetic Data Generation
- Temporal Time Process
- Applications
*Please note that some papers may belong to multiple categories. However, I've organized them according to their most significant contribution (purely subjective).
| Keyword | Title | Publisher | Date | Code | Notes |
|---|---|---|---|---|---|
| GBMCI | A Gradient Boosting Algorithm for Survival Analysis via Direct Optimazation of Concordance Index | Computational and Mathematical Methods in Medicine | 2013.09 | R | |
| Survival-CRPS | Countdown Regression: Sharp and Calibrated Survival Predictions | UAI | 2019 | PyTorch | |
| Bias in Cross-Entropy-Based Training of Deep Survival Networks | TPAMI | 2020.03 | |||
| SFM | Calibration and Uncertainty in Neural Time-to-Event Modeling | TNNLS | 2020.09 | TensorFlow | |
| X-CAL | X-CAL: Explicit Calibration for Survival Analysis | NeurIPS | 2020 | PyTorch | Poster |
| Discrete-RPS | Estimating Calibrated Individualized Survival Curves with Deep Learning | AAAI | 2021.02 | PyTorch | |
| KL-Calibration | Simpler Calibration for Survival Analysis | ICLR OpenReview | 2021.10 | ||
| SuMo-net | Survival regression with proper scoring rules and monotonic neural networks | AIStats | 2022.03 | PyTorch | |
| DQS | Proper Scoring Rules for Survival Analysis | ICML | 2023.06 | PyTorch | Poster |
| Keyword | Title | Publisher | Date | Code | Notes |
|---|---|---|---|---|---|
| SPIE | Simultaneous Prediction Intervals for Patient-Specific Survival Curves | IJCAI | 2019 | Python | |
| SurvLIME | SurvLIME: A method for explaining machine learning survival models | Knowledge-Based Systems | 2020.09 | Python | |
| AutoScore-Survival | AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data | Journal of Biomedical Informatics | 2022.01 | R | |
| EXCEL | Explainable Censored Learning: Finding Critical Features with Long Term Prognostic Values for Survival Prediction | Arxiv | 2022.09 | ||
| BNN-ISD | Using Bayesian Neural Networks to Select Features and Compute Credible Intervals for Personalized Survival Prediction | IEEE TBME | 2023.07 | PyTorch |
| Keyword | Title | Publisher | Date | Code | Notes |
|---|---|---|---|---|---|
| On pseudo-values for regression analysis in competing risks models | Lifetime Data Analysis | 2009.06 | |||
| DMGP | Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks | NeurIPS | 2017.12 | ||
| DeepHit | DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks | AAAI | 2018.02 | TensorFlow | |
| DSM | Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks | IEEE JBHI | 2021.01 | Pytorch | |
| HSA | A Hierarchical Approach to Multi-Event Survival Analysis | AAAI | 2021.05 | PyTorch | |
| SurvTRACE | SurvTRACE: Transformers for Survival Analysis with Competing Events | Arxiv | 2021.10 | Pytorch | |
| Deep-CSA | Deep-CSA: Deep Contrastive Learning for Dynamic Survival Analysis with Competing Risks | IEEE JBHI | 2022.04 | ||
| DeepPseudo | DeepPseudo: Pseudo Value Based Deep Learning Models for Competing Risk Analysis | KDD DSHealth Workshop | 2022.08 |
| Title | Publisher | Date | Code | Notes | |
|---|---|---|---|---|---|
| Pseudo-observations | Pseudo-observations in survival analysis | Statistical Methods in Medical Research | 2010 | ||
| A doubly robust censoring unbiased transformation | The International Journal of Biostatistics | 2007.03 | |||
| Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting | Journal of Biomedical Informatics | 2016.03 | R | ||
| A General Machine Learning Framework for Survival Analysis | ECML | 2020.06 | R | ||
| CSA | Conformalized survival analysis | JRSS: Series B | 2023.01 | R | |
| Adaptive-CSA | Conformalized survival analysis with adaptive cut-offs | Biometrika | 2023.12 | R | |
| CSD | Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration | ICML | 2024.05 | Python | Poster |
| CSD-iPOT | Toward Conditional Distribution Calibration in Survival Prediction | NeurIPS | 2024.10 | Python |
| Keyword | Title | Publisher | Date | Code | Notes |
|---|---|---|---|---|---|
| Causal inference in survival analysis using pseudo-observations | Statistics in Medicine | 2017.03 | |||
| CausalTree | Causal Inference for Survival Analysis | Arvix | 2018.03 | R | |
| CSA | Enabling Counterfactual Survival Analysis with Balanced Representations | ACM CHIL | 2021.03 | Python | |
| SurvITE | SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data | NeurIPS | 2021.10 | TensorFlow | |
| CMHE | Counterfactual Phenotyping with Censored Time-to-Events | KDD | 2022.02 | PyTorch | |
| DNMC | Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data | AISTATS | 2022.03 | TensorFlow | |
| compCATE | Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data | AISTATS | 2023.02 | Python |
| Keyword | Title | Publisher | Date | Code | Notes |
|---|---|---|---|---|---|
| FSRF | Longitudinal Fairness with Censorship | AAAI | 2022.03 | ||
| FISA | Fair and Interpretable Models for Survival Analysis | KDD | 2022.08 | Video | |
| IFS | Censored Fairness through Awareness | AAAI | 2023.03 | ||
| Fairness-Aware Processing Techniques in Survival Analysis: Promoting Equitable Predictions | ECML-PKDD | 2023.09 |
| Keyword | Title | Publisher | Date | Code | Notes |
|---|---|---|---|---|---|
| CopulaDeepSurvival | Copula-Based Deep Survival Models for Dependent Censoring | UAI | 2023.06 | PyTorch | |
| DCSurvival | Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees | AAAI | 2023.12 | PyTorch | |
| PSA | Proximal survival analysis to handle dependent right censoring | JRSS: Series B | 2024.05 |
| Keyword | Title | Publisher | Date | Code | Notes |
|---|---|---|---|---|---|
| SurvivalGAN | SurvivalGAN: Generating Time-to-Event Data for Survival Analysis | AIStats | 2023.02 | PyTorch | |
| Conditioning on Time is All You Need for Synthetic Survival Data Generation | Arxiv | 2024.05 | PyTorch |
| Title | Publisher | Date | Code | Notes |
|---|---|---|---|---|
| Lecture Notes: Temporal Point Processes and the Conditional Intensity Function | Arxiv | 2018.06 | ||
| Temporal Point Processes | Course Material | 2019.01 | ||
| Recent Advance in Temporal Point Process: from Machine Learning Perspective | 2019 | |||
| Wavelet Reconstruction Networks for Marked Point Processes | AAAI Spring Symposium (SP-ACA) | 2021.03 | Python | |
| Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations | ICLR | 2024.01 |
