Discrete Time Neural Network Models to Address Time-Varying Predictor Importance: An Illustration in Predicting Mortality Over Different Time Horizons

Clinical predictive models (CPMs) are crucial for forecasting patient outcomes using available electronic health record (EHR) data. Traditional time-to-event (TTE) models, like the Cox proportional hazards model, assume that hazard ratios remain constant over time, which may not hold in many clinical settings. In this study, we introduce a Discrete Time Neural Network (DTNN) to address these limitations by modeling time-varying predictor importance. The DTNN combines the flexibility of classification models with the advantage of handling time-to-event data, providing a single model fit across multiple time horizons. Using data from patients with end-stage kidney disease (ESKD) undergoing hemodialysis, we demonstrate that the DTNN can flexibly adjust for different risk factors across short-term and long-term mortality predictions. The model was evaluated using cumulative-dynamic area under the receiver operating characteristic (CD-AUROC) to compare patients who remained event-free up to a given time t with those who experienced the event before t. Our results show that the DTNN outperforms traditional TTE models and provides robust predictions across varying time intervals, making it an appealing choice for clinical settings where time-varying predictor importance is essential.