Evidential time-to-event prediction with calibrated uncertainty quantification

TL;DR

This paper introduces an evidential regression model for time-to-event prediction that effectively quantifies uncertainty, handles censored data, and outperforms existing methods in clinical survival analysis.

Contribution

The paper presents a novel evidential regression approach for survival analysis that models both epistemic and aleatory uncertainties using belief functions and Gaussian fuzzy numbers.

Findings

Outperforms state-of-the-art methods in accuracy and reliability

Effectively handles censored data in survival analysis

Provides uncertainty-aware predictions for clinical decision-making

Abstract

Time-to-event analysis provides insights into clinical prognosis and treatment recommendations. However, this task is more challenging than standard regression problems due to the presence of censored observations. Additionally, the lack of confidence assessment, model robustness, and prediction calibration raises concerns about the reliability of predictions. To address these challenges, we propose an evidential regression model specifically designed for time-to-event prediction. The proposed model quantifies both epistemic and aleatory uncertainties using Gaussian Random Fuzzy Numbers and belief functions, providing clinicians with uncertainty-aware survival time predictions. The model is trained by minimizing a generalized negative log-likelihood function accounting for data censoring. Experimental evaluations using simulated datasets with different data distributions and censoring conditions, as well as real-world datasets across diverse clinical applications, demonstrate that our model delivers both accurate and reliable performance, outperforming state-of-the-art methods. These results highlight the potential of our approach for enhancing clinical decision-making in survival analysis.