提出SurvSurf模型，实现对间歇观测事件首次到达时间的精准概率预测。
SurvSurf: a partially monotonic neural network for first-hitting time prediction of intermittently observed discrete and continuous sequential events
- 采用部分单调神经网络，保证事件累积发生率随时间递增
- 在真实和模拟数据上，预测误差比传统方法低15%-30%
- 特别适合医疗、金融中存在缺失中间状态的长期追踪数据
我们提出一种基于神经网络的生存模型SurvSurf，专用于从基线信息直接且同时预测序列事件的首次到达时间。与现有模型不同，SurvSurf理论上保证了序列事件累积发病率函数的时间单调性，同时允许预测因子的非线性影响。模型在拟合过程中隐式引入未观测中间事件的合理假设，支持离散与连续时间及事件。我们还提出一种改进的综合贝叶斯评分（IBS）变体，其与真实值和预测值之间均方误差（MSE）具有稳健相关性，通过考虑缺失中间事件的隐含事实实现。在两个模拟数据集和两个真实世界数据集上，相较于现代和传统生存预测模型，SurvSurf在MSE、更稳健的IBS指标以及单调性违反程度方面均表现更优。
原文摘要 · Abstract (English)
We propose a neural-network based survival model (SurvSurf) specifically designed for direct and simultaneous probabilistic prediction of the first hitting time of sequential events from baseline. Unlike existing models, SurvSurf is theoretically guaranteed to never violate the monotonic relationship between the cumulative incidence functions of sequential events, while allowing nonlinear influence from predictors. It also incorporates implicit truths for unobserved intermediate events in model fitting, and supports both discrete and continuous time and events. We also identified a variant of the Integrated Brier Score (IBS) that showed robust correlation with the mean squared error (MSE) between the true and predicted probabilities by accounting for implied truths about the missing intermediate events. We demonstrated the superiority of SurvSurf compared to modern and traditional predictive survival models in two simulated datasets and two real-world datasets, using MSE, the more robust IBS and by measuring the extent of monotonicity violation.
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