When data are right-censored, i.e. some outcomes are missing due to a limited
period of observation, survival analysis can compute the "time to event".
Multiple classes of outcomes lead to a classification variant: predicting the
most likely event, known as competing risks, which has been less studied. To
build a loss that estimates outcome probabilities for such settings, we
introduce a strictly proper censoring-adjusted separable scoring rule that can
be optimized on a subpart of the data because the evaluation is made
independently of observations. It enables stochastic optimization for competing
risks which we use to train gradient boosting trees. Compared to 11
state-of-the-art models, this model, MultiIncidence, performs best in
estimating the probability of outcomes in survival and competing risks. It can
predict at any time horizon and is much faster than existing alternatives.

当数据存在右侧截断情况时，生存分析可以计算 “事件发生时间”。多类别的结果导致了一种分类变种：预测最可能的事件，即已知的竞争风险，这方面的研究较少。为了构建一个估计这种情况下的结果概率的损失函数，我们引入了一个严格正确的修正分离的评分规则，这个规则可以在部分数据上进行优化，因为评估是独立于观测的。它可以用于训练梯度提升树来进行竞争风险分析。与 11 种现有模型相比，这个模型 “MultiIncidence” 在生存和竞争风险的结果概率估计上表现最佳。它可以在任何时间点进行预测，并且比现有的替代方法快得多。