Deep Learning Survival Analysis for Competing Risk with Functional Covariates and Missing Imputation

Music City Center 

Discharging patients from the intensive care unit (ICU) marks an important moment in their recovery-it's a transition from acute care to a lower level of dependency. However, even after leaving the ICU, many patients still face serious risks for adverse outcomes, such as ICU readmission due to complications or subsequent in-hospital death. In this study, we develop a unified deep-learning framework for competing risk modeling to improve the prediction performance on ICU patient outcomes. The proposed approach integrates discrete-time survival models with specially designed neural network architectures, which could handle complex data structures consisting of functional covariates and missing data. By incorporating gradient-based imputation techniques with discrete-time modeling, this framework allows for precise interval-based risk estimation, explicitly addressing the complexities arising from competing events, such as disease progression and mortality, which may censor each other. In addition, we validate the effectiveness of our method using two large ICU datasets and several simulated datasets, demonstrating improved prediction accuracy and generalizability over traditional models. The proposed framework effectively captures the dynamic interactions between various risk factors and their impact on patient outcomes.

Competing Risks

Discrete-time Survival

Deep Learning

functional data

Missing Imputation

Critical care