mlsanityData to 14 Oct 2026

ICTSurF: Implicit Continuous-Time Survival Functions with Neural Networks

arXiv 2312.05818 ↗First posted 10 Dec 2023cs.LGcited by 0

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Abstract (arXiv)

Survival analysis is a widely known method for predicting the likelihood of an event over time. The challenge of dealing with censored samples still remains. Traditional methods, such as the Cox Proportional Hazards (CPH) model, hinge on the limitations due to the strong assumptions of proportional hazards and the predetermined relationships between covariates. The rise of models based on deep neural networks (DNNs) has demonstrated enhanced effectiveness in survival analysis. This research introduces the Implicit Continuous-Time Survival Function (ICTSurF), built on a continuous-time survival model, and constructs survival distribution through implicit representation. As a result, our method is capable of accepting inputs in continuous-time space and producing survival probabilities in continuous-time space, independent of neural network architecture. Comparative assessments with existing methods underscore the high competitiveness of our proposed approach. Our implementation of ICTSurF is available at https://github.com/44REAM/ICTSurF.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 30 references that are in the map. First posted on arXiv 10 Dec 2023.

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14 of 30 references.

References · 14

  1. 2017Attention Is All You Needcited by 60,361
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  7. 2023Deep Learning for Survival Analysis: A Reviewcited by 75
  8. 2018A Scalable Discrete-Time Survival Model for Neural Networkscited by 72
  9. 2019Continuous and Discrete-Time Survival Prediction with Neural Networkscited by 70
  10. 2020Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Riskscited by 70
  11. 2021SurvTRACE: Transformers for Survival Analysis with Competing Eventscited by 41
  12. 2022auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Datacited by 17
2 more
  1. 2020Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysiscited by 11
  2. 2023Learning Survival Distribution with Implicit Survival Functioncited by 1

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