Recently developed survival analysis methods improve upon existing approaches
by predicting the probability of event occurrence in each of a number
pre-specified (discrete) time intervals. By avoiding placing strong parametric
assumptions on the event density, this approach tends to improve prediction
performance, particularly when data are plentiful. However, in clinical
settings with limited available data, it is often preferable to judiciously
partition the event time space into a limited number of intervals well suited
to the prediction task at hand. In this work, we develop a method to learn from
data a set of cut points defining such a partition. We show that in two
simulated datasets, we are able to recover intervals that match the underlying
generative model. We then demonstrate improved prediction performance on three
real-world observational datasets, including a large, newly harmonized stroke
risk prediction dataset. Finally, we argue that our approach facilitates
clinical decision-making by suggesting time intervals that are most appropriate
for each task, in the sense that they facilitate more accurate risk prediction.

最近发展的生存分析方法通过在每个预先确定的（离散）时间间隔内预测事件发生的概率来改进现有方法。本研究提出了一种从数据中学习划分事件时间间隔的方法，并在两个模拟数据集和三个真实世界观察数据集上展示了改进的预测性能。我们认为这种方法通过指导最适合每个任务的时间间隔来促进临床决策。