An evidential time-to-event prediction model based on Gaussian random fuzzy numbers
This work addresses uncertainty quantification in survival analysis for domains like healthcare or reliability engineering, but it appears incremental as it builds on existing evidential and fuzzy methods.
The authors tackled time-to-event prediction with censored data by introducing an evidential model using Gaussian random fuzzy numbers to quantify uncertainty, achieving very good performance compared to state-of-the-art methods in experiments on two real-world datasets.
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.