A Temporal Convolutional Network Deep Cox Mixtures Model for Dynamic Risk Prediction

Dynamic risk prediction is an important statistical technique for detecting temporal changes in risk and provides quantitative support for early risk identification in clinical decision-making, industrial process monitoring, and financial anomaly detection. This study proposes a Temporal Convolutional Network Deep Cox Mixtures model (TCN-DCM) for longitudinal survival data by integrating a Temporal Convolutional Network, which learns temporal patterns from longitudinal covariates, with a Deep Cox Mixture framework that relaxes the conventional proportional hazards assumption. Simulation studies were conducted to compare the proposed model with existing deep learning-based methods, including Recurrent Deep Survival Machines and Dynamic-DeepHit, as well as the traditional joint model. The results showed that, when the proportional hazards assumption held, TCN-DCM outperformed the existing deep learning-based models. When the proportional hazards assumption was violated, TCN-DCM achieved predictive performance comparable to that of Recurrent Deep Survival Machines and yielded superior results for some evaluation metrics. The proposed model was further applied to a primary biliary cholangitis dataset, where it achieved the best overall predictive performance and illustrated dynamic individualized survival risk prediction. These findings indicate that TCN-DCM provides a flexible and broadly applicable approach for dynamic risk prediction in longitudinal survival analysis.