提升生存预测的个体化校准精度，让预测更可信。
Toward Conditional Distribution Calibration in Survival Prediction
- 基于置信性预测，利用个体在观测时间点的生存概率进行校准。
- 在15个真实数据集上同时提升边际与条件校准效果。
- 理论保证可靠，适合临床决策等对个体预测要求高的场景。
生存预测常需从存在删失的数据中估计事件发生时间分布。以往方法侧重提升区分度和边际校准。本文强调条件校准在实际应用中的重要性，尤其在个体决策中的作用。提出一种基于置信性预测的方法，利用模型在该实例观测时间点的个体生存概率进行校准。该方法有效提升了模型的边际与条件校准性能，且不损害区分度。提供了边际与条件校准的渐近理论保证，并在15个多样化的真实世界数据集上进行了广泛测试，验证了方法在多种场景下的实用性和普适性。
原文摘要 · Abstract (English)
Survival prediction often involves estimating the time-to-event distribution from censored datasets. Previous approaches have focused on enhancing discrimination and marginal calibration. In this paper, we highlight the significance of conditional calibration for real-world applications -- especially its role in individual decision-making. We propose a method based on conformal prediction that uses the model's predicted individual survival probability at that instance's observed time. This method effectively improves the model's marginal and conditional calibration, without compromising discrimination. We provide asymptotic theoretical guarantees for both marginal and conditional calibration and test it extensively across 15 diverse real-world datasets, demonstrating the method's practical effectiveness and versatility in various settings.
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