TdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction.

ABSTRACT: The aim of dynamic prediction is to provide individualized risk predictions over time, which are updated as new data become available. In pursuit of constructing a dynamic prediction model for a progressive eye disorder, age-related macular degeneration (AMD), we propose a time-dependent Cox survival neural network (tdCoxSNN) to predict its progression using longitudinal fundus images. tdCoxSNN builds upon the time-dependent Cox model by utilizing a neural network to capture the nonlinear effect of time-dependent covariates on the survival outcome. Moreover, by concurrently integrating a convolutional neural network with the survival network, tdCoxSNN can directly take longitudinal images as input. We evaluate and compare our proposed method with joint modelling and landmarking approaches

SUBMITTER: Zeng L 

PROVIDER: S-EPMC11725344 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature