# An evidential time-to-event prediction model based on Gaussian random fuzzy numbers

arXiv: https://arxiv.org/abs/2406.13487 · HTML: https://mlsanity.com/paper/2406.13487/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence](https://mlsanity.com/paper/2004.11638/) (2020)
- [Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: general framework and practical models](https://mlsanity.com/paper/2202.08081/) (2022)
- [An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers](https://mlsanity.com/paper/2208.00647/) (2022)

## Built on by (most cited first, 5 of 5)

- [Position: Stop Chasing the C-index when Evaluating Survival Analysis Models](https://mlsanity.com/paper/2506.02075/) (2025)
- [DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction](https://mlsanity.com/paper/2510.00053/) (2025)
- [Bridging the Modality Bottleneck in Pathology MIL through Virtual Molecular Staining](https://mlsanity.com/paper/2605.16392/) (2026)
- [Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysis](https://mlsanity.com/paper/2606.19966/) (2026)
- [Evidential Fusion Network for Multimodal Survival Prediction under Missing Modalities](https://mlsanity.com/paper/2606.20757/) (2026)

## Abstract (arXiv)

We introduce an evidential model for time-to-event prediction with censored data. In this model, uncertainty on event time is quantified by Gaussian random fuzzy numbers, a newly introduced family of random fuzzy subsets of the real line with associated belief functions, generalizing both Gaussian random variables and Gaussian possibility distributions. Our approach makes minimal assumptions about the underlying time-to-event distribution. The model is fit by minimizing a generalized negative log-likelihood function that accounts for both normal and censored data. Comparative experiments on two real-world datasets demonstrate the very good performance of our model as compared to the state-of-the-art.