  Real-time individual endpoint prediction has always been a challenging task
but of great clinic utility for both patients and healthcare providers. With
6,879 chronic kidney disease stage 4 (CKD4) patients as a use case, we explored
the feasibility and performance of gated recurrent units with decay that models
Weibull probability density function (GRU-D-Weibull) as a semi-parametric
longitudinal model for real-time individual endpoint prediction. GRU-D-Weibull
has a maximum C-index of 0.77 at 4.3 years of follow-up, compared to 0.68
achieved by competing models. The L1-loss of GRU-D-Weibull is ~66% of XGB(AFT),
~60% of MTLR, and ~30% of AFT model at CKD4 index date. The average absolute
L1-loss of GRU-D-Weibull is around one year, with a minimum of 40% Parkes
serious error after index date. GRU-D-Weibull is not calibrated and
significantly underestimates true survival probability. Feature importance
tests indicate blood pressure becomes increasingly important during follow-up,
while eGFR and blood albumin are less important. Most continuous features have
non-linear/parabola impact on predicted survival time, and the results are
generally consistent with existing knowledge. GRU-D-Weibull as a
semi-parametric temporal model shows advantages in built-in parameterization of
missing, native support for asynchronously arrived measurement, capability of
output both probability and point estimates at arbitrary time point for
arbitrary prediction horizon, improved discrimination and point estimate
accuracy after incorporating newly arrived data. Further research on its
performance with more comprehensive input features, in-process or post-process
calibration are warranted to benefit CKD4 or alike terminally-ill patients.
