Deep Neural Network for Survival Analysis of End-Stage Kidney Disease
Presentation Type
Poster
Student
Yes
Abstract
We are investigating the use of deep neural networks when performing survival Analysis. Traditionally, the Cox Proportional Hazard (PH) model has been used to find estimates for the parameters that affect the survival time for a given disease. However, the Cox PH model fails to capture the nonlinear behavior in the data. In this paper, we employ a deep survival model approach to model the survival time for a person with end-stage renal disease. The goal of the analysis is to find the effect of various factors on ESKD survival time. Our results indicate that, compared to the Cox PH model, the deep survival model provides more accurate results with Harrell's C-index. Providing preliminary results of deep survival neural networks on USRDS data.
Start Date
2-6-2024 1:00 PM
End Date
2-6-2024 2:00 PM
Deep Neural Network for Survival Analysis of End-Stage Kidney Disease
Volstorff A
We are investigating the use of deep neural networks when performing survival Analysis. Traditionally, the Cox Proportional Hazard (PH) model has been used to find estimates for the parameters that affect the survival time for a given disease. However, the Cox PH model fails to capture the nonlinear behavior in the data. In this paper, we employ a deep survival model approach to model the survival time for a person with end-stage renal disease. The goal of the analysis is to find the effect of various factors on ESKD survival time. Our results indicate that, compared to the Cox PH model, the deep survival model provides more accurate results with Harrell's C-index. Providing preliminary results of deep survival neural networks on USRDS data.