{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["74(1)"],"submitter":["Zeng L"],"pubmed_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 "],"journal":["Journal of the Royal Statistical Society. Series C, Applied statistics"],"pagination":["187-203"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11725344"],"repository":["biostudies-literature"],"pubmed_title":["tdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction."],"pmcid":["PMC11725344"],"pubmed_authors":["Ding Y","Zhang J","Zeng L","Chen W"],"additional_accession":[]},"is_claimable":false,"name":"tdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction.","description":"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 ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jan","modification":"2026-06-04T08:56:32.539Z","creation":"2026-05-07T03:12:03.606Z"},"accession":"S-EPMC11725344","cross_references":{"pubmed":["39807175"],"doi":["10.1093/jrsssc/qlae051"]}}