Time-to-event modelling, known as survival analysis, differs from standard regression as it addresses censoring in patients who do not experience the event of interest. Despite competitive performances in tackling this problem, machine learning methods often ignore other competing risks that preclude the event of interest. This practice biases the survival estimation. Extensions to address this challenge often rely on parametric assumptions or numerical estimations leading to sub-optimal survival approximations. This paper leverages constrained monotonic neural networks to model each competing survival distribution. This modelling choice ensures the exact likelihood maximisation at a reduced computational cost by using automatic differentiation. The effectiveness of the solution is demonstrated on one synthetic and three medical datasets. Finally, we discuss the implications of considering competing risks when developing risk scores for medical practice.

翻译：时间-事件建模，即生存分析，不同于标准回归，因为它处理了未经历感兴趣事件的患者中的删失问题。尽管机器学习方法在处理此问题上表现出竞争力，但常常忽略其他阻碍感兴趣事件的竞争风险。这种做法会导致生存估计产生偏差。解决这一挑战的扩展方法往往依赖于参数假设或数值估计，从而导致次优的生存近似。本文利用受限单调神经网络来建模每个竞争生存分布。这种建模选择通过使用自动微分，确保了精确似然最大化，同时降低了计算成本。该解决方案的有效性在一个合成数据集和三个医疗数据集上得到了验证。最后，我们讨论了在医疗实践中开发风险评分时考虑竞争风险的意义。