Evidential time-to-event prediction model with well-calibrated uncertainty estimation Article Swipe
Ling Huang
,
Yucheng Xing
,
Swapnil Mishra
,
Thierry Denœux
,
Mengling Feng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.07853
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.07853
Time-to-event analysis, or Survival analysis, provides valuable insights into clinical prognosis and treatment recommendations. However, this task is typically more challenging than other regression tasks due to the censored observations. Moreover, concerns regarding the reliability of predictions persist among clinicians, mainly attributed to the absence of confidence assessment, robustness, and calibration of prediction. To address those challenges, we introduce an evidential regression model designed especially for time-to-event prediction tasks, with which the most plausible event time, is directly quantified by aggregated Gaussian random fuzzy numbers (GRFNs). The GRFNs are a newly introduced family of random fuzzy subsets of the real line that generalizes both Gaussian random variables and Gaussian possibility distributions. Different from conventional methods that construct models based on strict data distribution, e.g., proportional hazard function, our model only assumes the event time is encoded in a real line GFRN without any strict distribution assumption, therefore offering more flexibility in complex data scenarios. Furthermore, the epistemic and aleatory uncertainty regarding the event time is quantified within the aggregated GRFN as well. Our model can, therefore, provide more detailed clinical decision-making guidance with two more degrees of information. The model is fit by minimizing a generalized negative log-likelihood function that accounts for data censoring based on uncertainty evidence reasoning. Experimental results on simulated datasets with varying data distributions and censoring scenarios, as well as on real-world datasets across diverse clinical settings and tasks, demonstrate that our model achieves both accurate and reliable performance, outperforming state-of-the-art methods.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.07853
- https://arxiv.org/pdf/2411.07853
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4404399488Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2411.07853Digital Object Identifier
- Title
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Evidential time-to-event prediction model with well-calibrated uncertainty estimationWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-12Full publication date if available
- Authors
-
Ling Huang, Yucheng Xing, Swapnil Mishra, Thierry Denœux, Mengling FengList of authors in order
- Landing page
-
https://arxiv.org/abs/2411.07853Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2411.07853Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2411.07853Direct OA link when available
- Concepts
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Estimation, Event (particle physics), Computer science, Econometrics, Statistics, Mathematics, Engineering, Physics, Systems engineering, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |