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Calibration and Uncertainty in Neural Time-to-Event Modeling.

Publication ,  Journal Article
Chapfuwa, P; Tao, C; Li, C; Khan, I; Chandross, KJ; Pencina, MJ; Carin, L; Henao, R
Published in: IEEE Trans Neural Netw Learn Syst
April 2023

Models for predicting the time of a future event are crucial for risk assessment, across a diverse range of applications. Existing time-to-event (survival) models have focused primarily on preserving pairwise ordering of estimated event times (i.e., relative risk). We propose neural time-to-event models that account for calibration and uncertainty while predicting accurate absolute event times. Specifically, an adversarial nonparametric model is introduced for estimating matched time-to-event distributions for probabilistically concentrated and accurate predictions. We also consider replacing the discriminator of the adversarial nonparametric model with a survival-function matching estimator that accounts for model calibration. The proposed estimator can be used as a means of estimating and comparing conditional survival distributions while accounting for the predictive uncertainty of probabilistic models. Extensive experiments show that the distribution matching methods outperform existing approaches in terms of both calibration and concentration of time-to-event distributions.

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Published In

IEEE Trans Neural Netw Learn Syst

DOI

EISSN

2162-2388

Publication Date

April 2023

Volume

34

Issue

4

Start / End Page

1666 / 1680

Location

United States

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4602 Artificial intelligence
 

Citation

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MLA
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Chapfuwa, P., Tao, C., Li, C., Khan, I., Chandross, K. J., Pencina, M. J., … Henao, R. (2023). Calibration and Uncertainty in Neural Time-to-Event Modeling. IEEE Trans Neural Netw Learn Syst, 34(4), 1666–1680. https://doi.org/10.1109/TNNLS.2020.3029631
Chapfuwa, Paidamoyo, Chenyang Tao, Chunyuan Li, Irfan Khan, Karen J. Chandross, Michael J. Pencina, Lawrence Carin, and Ricardo Henao. “Calibration and Uncertainty in Neural Time-to-Event Modeling.IEEE Trans Neural Netw Learn Syst 34, no. 4 (April 2023): 1666–80. https://doi.org/10.1109/TNNLS.2020.3029631.
Chapfuwa P, Tao C, Li C, Khan I, Chandross KJ, Pencina MJ, et al. Calibration and Uncertainty in Neural Time-to-Event Modeling. IEEE Trans Neural Netw Learn Syst. 2023 Apr;34(4):1666–80.
Chapfuwa, Paidamoyo, et al. “Calibration and Uncertainty in Neural Time-to-Event Modeling.IEEE Trans Neural Netw Learn Syst, vol. 34, no. 4, Apr. 2023, pp. 1666–80. Pubmed, doi:10.1109/TNNLS.2020.3029631.
Chapfuwa P, Tao C, Li C, Khan I, Chandross KJ, Pencina MJ, Carin L, Henao R. Calibration and Uncertainty in Neural Time-to-Event Modeling. IEEE Trans Neural Netw Learn Syst. 2023 Apr;34(4):1666–1680.

Published In

IEEE Trans Neural Netw Learn Syst

DOI

EISSN

2162-2388

Publication Date

April 2023

Volume

34

Issue

4

Start / End Page

1666 / 1680

Location

United States

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4602 Artificial intelligence