GRU-D-Weibull: A Novel Real-Time Individualized Endpoint Prediction

Background: Accurate risk prediction models for individual level endpoint (e.g., death), or time-to-endpoint are highly desirable in clinical practice. Methods: We propose a novel predictive modeling approach, GRU-D-Weibull, which models Weibull distribution leveraging gated recurrent units with decay (GRU-D), for real-time individualized endpoint prediction and population level risk management using electronic health records (EHRs). Experiments: We systematically evaluated the performance and showcased the clinical utility of the proposed approach through individual level endpoint prediction using a cohort of patients with chronic kidney disease stage 4 (CKD4). A total of 536 features including ICD/CPT codes, medications, lab tests, vital measurements, and demographics were retrieved for 6,879 CKD4 patients. The performance metrics including C-index, L1-loss, Parkes' error, and predicted survival probability at time of event were compared between GRU-D-Weibull and other alternative approaches including accelerated failure time model (AFT), XGBoost(AFT), random survival forest (RSF), and Nnet-survival. Both in-process and post-process calibrations were experimented on GRU-D-Weibull generated survival probabilities. Results: GRU-D-Weibull demonstrated C-index of ~0.7 at index date, which increased to ~0.77 at 4.3 years of follow-up, comparable to that of RSF. GRU-D-Weibull achieved absolute L1-loss of ~1.1 years (sd 0.95) at CKD4 index date, and a minimum of ~0.45 year (sd 0.3) at 4 years of follow-up, comparing to second-ranked RSF of ~1.4 years (sd 1.1) at index date and ~0.64 years (sd 0.26) at 4 years. Both significantly outperform competing approaches. GRU-D-Weibull constrained predicted survival probability at time of event to a remarkably smaller and more fixed range than competing models throughout follow-up.