Neural Fine-Gray: Monotonic neural networks for competing risks
Vincent Jeanselme Chang Ho Yoon Brian Tom Jessica Barrett
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.