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Time-to-Event Prediction with Neural Networks and Cox Regression

arXiv 1907.00825 ↗First posted 1 Jul 2019stat.ML cs.LGcited by 173

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Abstract (arXiv)

New methods for time-to-event prediction are proposed by extending the Cox proportional hazards model with neural networks. Building on methodology from nested case-control studies, we propose a loss function that scales well to large data sets, and enables fitting of both proportional and non-proportional extensions of the Cox model. Through simulation studies, the proposed loss function is verified to be a good approximation for the Cox partial log-likelihood. The proposed methodology is compared to existing methodologies on real-world data sets, and is found to be highly competitive, typically yielding the best performance in terms of Brier score and binomial log-likelihood. A python package for the proposed methods is available at https://github.com/havakv/pycox.

Key facts. Cited by 173 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 22; 2022: 27; 2023: 30; 2024: 29; 2025: 37; 2026: 28.

173 papers.

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  1. 2021A survey on datasets for fairness-aware machine learningcited by 157
  2. 2023Deep Learning for Survival Analysis: A Reviewcited by 75
  3. 2024Multimodal Prototyping for cancer survival predictioncited by 47
  4. 2021Deep Cox Mixtures for Survival Regressioncited by 43
  5. 2021Survival Regression with Proper Scoring Rules and Monotonic Neural Networkscited by 42
  6. 2021A Deep Variational Approach to Clustering Survival Datacited by 25
  7. 2022SurvSHAP(t): Time-dependent explanations of machine learning survival modelscited by 24
  8. 2024CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysiscited by 22
  9. 2022Longitudinal Fairness with Censorshipcited by 21
  10. 2022auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Datacited by 17
  11. 2024An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomescited by 16
  12. 2022SurvSet: An open-source time-to-event dataset repositorycited by 15
38 more
  1. 2023Fair Decision-making Under Uncertaintycited by 15
  2. 2022Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographicscited by 14
  3. 2024Fairness in Survival Analysis with Distributionally Robust Optimizationcited by 13
  4. 2021Medical Imaging and Machine Learningcited by 12
  5. 2022DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysiscited by 12
  6. 2023Individual Fairness under Uncertaintycited by 11
  7. 2024Conformalized Survival Distributions: A Generic Post-Process to Increase Calibrationcited by 10
  8. 2022Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guaranteecited by 9
  9. 2022Survival Mixture Density Networkscited by 9
  10. 2023Proper Scoring Rules for Survival Analysiscited by 9
  11. 2023DySurv: dynamic deep learning model for survival analysis with conditional variational inferencecited by 9
  12. 2021T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLPcited by 8
  13. 2021The structure of online social networks modulates the rate of lexical changecited by 8
  14. 2021Inverse-Weighted Survival Gamescited by 8
  15. 2022The Concordance Index decomposition: A measure for a deeper understanding of survival prediction modelscited by 8
  16. 2023MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Featurescited by 8
  17. 2023Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guaranteescited by 8
  18. 2021DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysiscited by 7
  19. 2021Avoiding C-hacking when evaluating survival distribution predictions with discrimination measurescited by 7
  20. 2022Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Datacited by 7
  21. 2022Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Datacited by 7
  22. 2022FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysiscited by 7
  23. 2023Federated Survival Forestscited by 7
  24. 2024Dynamical Survival Analysis with Controlled Latent Statescited by 7
  25. 2024HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Riskscited by 7
  26. 2024Evidential time-to-event prediction with calibrated uncertainty quantificationcited by 7
  27. 2022Extending the Neural Additive Model for Survival Analysis with EHR Datacited by 6
  28. 2023Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressionscited by 6
  29. 2023Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomicscited by 6
  30. 2021Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Modelscited by 5
  31. 2022Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysiscited by 5
  32. 2023Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Recordscited by 5
  33. 2024Optimal Sparse Survival Treescited by 5
  34. 2024Dynamic Survival Analysis for Early Event Predictioncited by 5
  35. 2024TorchSurv: A Lightweight Package for Deep Survival Analysiscited by 5
  36. 2024An evidential time-to-event prediction model based on Gaussian random fuzzy numberscited by 5
  37. 2024Toward Conditional Distribution Calibration in Survival Predictioncited by 5
  38. 2025Position: Stop Chasing the C-index when Evaluating Survival Analysis Modelscited by 5

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