Conformal prediction with censored data using Kaplan-Meier method

Xiaolin Sun, Yanhua Wang*

*此作品的通讯作者

科研成果: 期刊稿件会议文章同行评审

摘要

In this paper, we introduce a prediction algorithm founded on conformal prediction, tailored for constructing prediction intervals in the context of censored survival data. Conformal prediction frameworks distinguish themselves from other prediction paradigms by their non-empirical evaluation, reliance on user-defined confidence intervals for modeling errors, and widespread adoption across regression and classification methodologies, inclusive of survival analysis, in recent years. Herein, we present a novel application wherein the Kaplan-Meier method is employed to compute empirical quantiles of nonconformal scores, specifically tailored for censored schematic variables. This novel approach facilitates the generation of well-calibrated prediction intervals for survival times, augmenting any existing survival prediction algorithm. Validation of its efficacy and computational efficiency is performed on both the real-world dataset 'SUPPORT' and the synthetic dataset 'RRNLNPH.'

源语言英语
文章编号012030
期刊Journal of Physics: Conference Series
2898
1
DOI
出版状态已出版 - 2024
活动2024 2nd International Conference on Applied Statistics, Modeling and Advanced Algorithms, ASMA 2024 - Harbin, 中国
期限: 27 9月 202429 9月 2024

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