tdCoxSNN: Time-Dependent Cox Survival Neural Network for Continuous-time Dynamic Prediction
Lang Zeng, Jipeng Zhang, Wei Chen, Ying Ding

TL;DR
This paper introduces tdCoxSNN, a neural network-based model that dynamically predicts disease progression using longitudinal data and images, outperforming traditional methods in accuracy.
Contribution
The paper presents a novel time-dependent Cox survival neural network integrating CNNs for direct use of longitudinal images, enhancing dynamic prediction accuracy.
Findings
Outperforms joint modeling and landmarking approaches in simulations.
Achieves high predictive accuracy on AMD and PBC datasets.
Effectively incorporates longitudinal images into survival 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 non-linear effect of time-dependent covariates on the survival outcome. Moreover, by concurrently integrating a convolutional neural network (CNN) with the survival network, tdCoxSNN can directly take longitudinal images as input. We evaluate and compare our proposed method with joint modeling and landmarking approaches through extensive simulations. We applied the proposed approach to two real datasets. One is…
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Taxonomy
TopicsRetinal Imaging and Analysis · Retinal Diseases and Treatments · Retinal and Optic Conditions
