Copula‐Based Deep Learning Models for Competing Risks

This study introduces a novel approach to modeling competing risks in survival analysis by integrating learnable Copula functions (Clayton, Frank, and Gaussian) with deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model. Here, we are interested in classifying competing risks outcomes. The proposed method captures complex dependencies within the data. Our approach demonstrates improved predictive performance in survival data modeling by effectively capturing intricate dependency structures and event relationships. We validate the proposed models using both simulated data and real-world clinical data. This research highlights the potential of integrating Copula-based dependency structures into deep learning models for survival analysis with competing risks. The results emphasize how Copula-based neural networks can enhance prediction accuracy and handle competing risks in survival analysis.