Simultaneous Prediction Intervals for Patient-Specific Survival Curves
- Samuel Sokota
- Ryan D'Orazio
- Khurram Javed
- Humza Haider
- Russ Greiner, Dept of Computing Science; PI of AICML
Accurate models of patient survival probabilities provide important information to clinicians prescribing care for life threatening and terminal ailments. A recently developed class of models – known as individual survival distributions (ISDs) – produces patient-specific survival functions that offer greater descriptive power of patient outcomes than was previously possible. Unfortunately, at the time of writing, ISD models almost universally lack uncertainty quantification. In this paper we demonstrate that an existing method for estimating simultaneous prediction intervals from samples can easily be adapted for patient-specific survival curve analysis and yields accurate results. Furthermore, we introduce both a modification to the existing method and a novel method for estimating simultaneous prediction intervals and show that they offer competitive performance. It is worth emphasizing that these methods are not limited to survival analysis and can be applied in any context in which sampling the distribution of interest is tractable. Code is available at https://github.com/ssokota/spie.
Citation
S. Sokota, R. D'Orazio, K. Javed, H. Haider, R. Greiner. "Simultaneous Prediction Intervals for Patient-Specific Survival Curves". International Joint Conference on Artificial Intelligence (IJCAI), pp 5975-5981, August 2019.| Keywords: | Survival Prediction, Error Bars, Credible Regions |
| Category: | In Conference |
| Web Links: | IJCAI |
BibTeX
@incollection{Sokota+al:IJCAI19,
author = {Samuel Sokota and Ryan D'Orazio and Khurram Javed and Humza Haider
and Russ Greiner},
title = {Simultaneous Prediction Intervals for Patient-Specific Survival
Curves},
Pages = {5975-5981},
booktitle = {International Joint Conference on Artificial Intelligence
(IJCAI)},
year = 2019,
}Last Updated: February 06, 2020Submitted by Sabina P
