Summary of Evidential Time-to-event Prediction with Calibrated Uncertainty Quantification, by Ling Huang et al.
Evidential time-to-event prediction with calibrated uncertainty quantification
by Ling Huang, Yucheng Xing, Swapnil Mishra, Thierry Denoeux, Mengling Feng
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed evidential regression model is designed specifically for time-to-event prediction, addressing challenges such as censored observations, lack of confidence assessment, and prediction calibration. By quantifying both epistemic and aleatory uncertainties using Gaussian Random Fuzzy Numbers and belief functions, the model provides 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 demonstrate that the proposed model delivers both accurate and reliable performance, outperforming state-of-the-art methods across diverse clinical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces an advanced method for predicting how long it takes for something to happen (like when someone will get sick or have surgery). Right now, these predictions can be tricky because some of the data is missing or limited. The new approach uses special math tools to show just how sure we are about our predictions, which is important for making good decisions in healthcare. The method was tested on real-world data from different medical areas and showed it’s better than current methods. |
Keywords
» Artificial intelligence » Log likelihood » Regression
